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A Fine-Grained RecognitionModel based on Discriminative Region Localization and Efficient Second-Order Feature Encoding 认领 引用
1
作者 Xiaorui Zhang Yingying Wang +3 位作者 Wei Sun Shiyu Zhou Haoming Zhang Pengpai Wang 《Computers, Materials & Continua》 SCIE EI 2026年第4期946-965,共20页
Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in comp... Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in complex backgrounds,small target objects,and limited training data,leading to poor recognition.Fine-grained images exhibit“small inter-class differences,”and while second-order feature encoding enhances discrimination,it often requires dual Convolutional Neural Networks(CNN),increasing training time and complexity.This study proposes a model integrating discriminative region localization and efficient second-order feature encoding.By ranking feature map channels via a fully connected layer,it selects high-importance channels to generate an enhanced map,accurately locating discriminative regions.Cropping and erasing augmentations further refine recognition.To improve efficiency,a novel second-order feature encoding module generates an attention map from the fourth convolutional group of Residual Network 50 layers(ResNet-50)and multiplies it with features from the fifth group,producing second-order features while reducing dimensionality and training time.Experiments on Caltech-University of California,San Diego Birds-200-2011(CUB-200-2011),Stanford Car,and Fine-Grained Visual Classification of Aircraft(FGVC Aircraft)datasets show state-of-the-art accuracy of 88.9%,94.7%,and 93.3%,respectively. 展开更多
关键词 Fine-grained recognition feature encoding data augmentation second-order feature discriminative regions
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Multi-scale keypoints detection and motion features extraction in dairy cows using ResNet101-ASPP network 认领 引用
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作者 Saisai Wu Shuqing Han +5 位作者 Jing Zhang Guodong Cheng Yali Wang Kai Zhang Mingming Han Jianzhai Wu 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第5期2028-2040,共13页
Detecting keypoints in dairy cows aims to locate and track the motion trajectories of the body's joints,which plays a crucial role in behavior analysis and lameness detection.However,real farming scenarios,charact... Detecting keypoints in dairy cows aims to locate and track the motion trajectories of the body's joints,which plays a crucial role in behavior analysis and lameness detection.However,real farming scenarios,characterized by occlusions and large variations in object scale may result in poor detection results.Therefore,we introduce the atrous spatial pyramid pooling(ASPP) module into the shallow layers network of ResNet101,designed to improve the multi-scale feature extraction capability of the model.The ASPP module enhances the robustness of recognition for different dimensional sizes and occluded keypoints using different dilatation rates in the parallel atrous convolutional layers to expand the model's receptive field.Furthermore,seven types of motion features,including tracking up,gait symmetry,step height balance,motion speed variability,head swing amplitude,head-neck slope and back curvature are extracted simultaneously by monitoring and tracking the motion trajectory of distinct keypoints.Several of these features represent innovative extraction models and attributes,first proposed in this study.Multiple models are trained and tested on datasets containing 2,385 frames for ablation experiments.The experiments show that,in comparison with the ResNet50,MobileNet_v2_1.0,and EfficientNet-b0backbone networks,the training error and test error of ResNet101 are reduced by 4.04-30.12 pixels and 3.81-28.14 pixels.Therefore,ResNet101 is used as the benchmark for subsequent model improvement by adding the ASPP module.The training error and test error of the ResNet101-ASPP network are reduced by 0.27 and 0.24 pixels,respectively,compared to the benchmark network.The prediction confidence improves by 1.65-2.50% at three different dairy cow object scales.In addition,the keypoints under different occlusion conditions improve considerably,especially for small-scale keypoints,demonstrating the capability of the ASPP module for multi-scale feature extraction.By analyzing the distribution of the seven features and health,mild lameness,and severe lameness in dairy cows,it is shown that all the different features play an important role in distinguishing between different levels of lameness. 展开更多
关键词 dairy cows multi-scale keypoints detection ResNet101-ASPP network motion features
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DL-YOLO:AMulti-Scale Feature Fusion Detection Algorithm for Low-Light Environments 认领 引用
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作者 Yuanmeng Chang Hongmei Liu 《Computers, Materials & Continua》 SCIE EI 2026年第5期1901-1915,共15页
Driven by rapid advances in deep learning,object detection has been widely adopted across diverse application scenarios.However,in low-light conditions,critical visual cues of target objects are severely degraded,posi... Driven by rapid advances in deep learning,object detection has been widely adopted across diverse application scenarios.However,in low-light conditions,critical visual cues of target objects are severely degraded,posing a significant challenge for accurate low-light object detection.Existing methods struggle to preserve discriminative features while maintaining semantic consistency between low-light and normal-light images.For this purpose,this study proposes a DL-YOLO model specially tailored for low-light detection.To mitigate target feature attenuation introduced by repeated downsampling,we design aMulti-Scale FeatureConvolution(MSF-Conv)module that captures rich,multi-level details via multi-scale feature learning,thereby reducing model complexity and computational cost.For feature fusion,we integrated the C3k2-DWRmodule by embedding the Dilation-wise Residual(DWR)mechanism into the 2-core optimized Cross Stage Partial(C3)framework,achieving efficient feature integration.In addition,we replace conventional localization losses with WIoU(Weighted Intersection over Union),which dynamically adjusts gradient gain according to sample quality,thereby improving localization robustness and precision.Experiments on the ExDark dataset demonstrate that DL-YOLO delivers strong low-light detection performance.The relevant code is published at http://gffzz188fe103f8f1460as56cwbw0owpvu6okq.ffgz.tsg.suse.edu.cn/cym0997/DL-YOLO. 展开更多
关键词 Multi-scale feature extraction object detection low-light environments ExDark dataset
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Multidimensional visual feature encoding and functional organization in the pigeon entopallium 认领 引用
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作者 Jun-Cai Zhu Min-Jie Zhu +4 位作者 Qing-Zhi He Peng Wu Xiao-Ke Niu Jiang-Tao Wang Zhi-Zhong Wang 《Zoological Research》 SCIE CSCD 2026年第2期487-502,共16页
Understanding how birds perceive and recognize visual objects remains a fundamental question in neuroscience.The entopallium,a key node in the avian tectofugal pathway,has long been implicated in complex visual proces... Understanding how birds perceive and recognize visual objects remains a fundamental question in neuroscience.The entopallium,a key node in the avian tectofugal pathway,has long been implicated in complex visual processing,yet its internal functional architecture remains incompletely understood.In this study,neuronal activity in the pigeon entopallium was systematically mapped using controlled visual stimuli that independently varied in color,shape,and motion.Recordings revealed marked hue selectivity that remained invariant across luminance levels,pronounced orientation tuning in response to shape stimuli,and robust direction selectivity for moving stimuli.Spatial mapping further revealed distinct functional segregation,with color-selective neurons localized anteroventrally,shape-selective neurons dorsally,and motion-selective neurons posteriorly.At the same time,partial overlap among these response classes was observed,with a subset of neurons exhibiting joint tuning across stimulus dimensions,suggesting an organizational scheme characterized by regional specialization and partial cross-feature integration.Notably,entopallium neurons exhibited a moderate level of visual feature integration and shared important functional properties with early to intermediate stages of mammalian visual processing.Together,these findings establish the entopallium as a major site for multidimensional visual analysis in birds and provide evidence for convergent principles underlying the evolution of complex visual systems across vertebrates. 展开更多
关键词 Entopallium Tectofugal pathway Feature encoding Functional organization Object recognition
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Global context-aware multi-scale feature iterative refinement for aviation-road traffic semantic segmentation 认领 引用
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作者 Mengyue ZHANG Shichun YANG +1 位作者 Xinjie FENG Yaoguang CAO 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期429-441,共13页
Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made re... Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made remarkable achievements in both fine-grained segmentation and real-time performance.However,when faced with the huge differences in scale and semantic categories brought about by the mixed scenes of aerial remote sensing and road traffic,they still face great challenges and there is little related research.Addressing the above issue,this paper proposes a semantic segmentation model specifically for mixed datasets of aerial remote sensing and road traffic scenes.First,a novel decoding-recoding multi-scale feature iterative refinement structure is proposed,which utilizes the re-integration and continuous enhancement of multi-scale information to effectively deal with the huge scale differences between cross-domain scenes,while using a fully convolutional structure to ensure the lightweight and real-time requirements.Second,a welldesigned cross-window attention mechanism combined with a global information integration decoding block forms an enhanced global context perception,which can effectively capture the long-range dependencies and multi-scale global context information of different scenes,thereby achieving fine-grained semantic segmentation.The proposed method is tested on a large-scale mixed dataset of aerial remote sensing and road traffic scenes.The results confirm that it can effectively deal with the problem of large-scale differences in cross-domain scenes.Its segmentation accuracy surpasses that of the SOTA methods,which meets the real-time requirements. 展开更多
关键词 Aviation-road traffic Flying cars Global context-aware Multi-scale feature iterative refinement Semantic segmentation
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Multi-scale feature fused stacked autoencoder and its application for soft sensor modeling 认领 引用 被引量：3
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作者 Zhi Li Yuchong Xia +2 位作者 Jian Long Chensheng Liu Longfei Zhang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第5期241-254,共14页
Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE... Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively. 展开更多
关键词 Multi-scale feature fusion Soft sensors Stacked autoencoders Computational chemistry Chemical processes Parameter estimation
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Research on Camouflage Target Detection Method Based on Edge Guidance and Multi-Scale Feature Fusion 认领 引用
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作者 Tianze Yu Jianxun Zhang Hongji Chen 《Computers, Materials & Continua》 SCIE EI 2026年第4期1676-1697,共22页
Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the backgroun... Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the background,camouflaged objects often exhibit vague boundaries and varying scales,making it challenging to accurately locate targets and delineate their indistinct edges.To address this,we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network(EGMFNet),which leverages edge-guided multi-scale integration for enhanced performance.The model incorporates two innovative components:a Multi-scale Fusion Module(MSFM)and an Edge-Guided Attention Module(EGA).These designs exploit multi-scale features to uncover subtle cues between candidate objects and the background while emphasizing camouflaged object boundaries.Moreover,recognizing the rich contextual information in fused features,we introduce a Dual-Branch Global Context Module(DGCM)to refine features using extensive global context,thereby generatingmore informative representations.Experimental results on four benchmark datasets demonstrate that EGMFNet outperforms state-of-the-art methods across five evaluation metrics.Specifically,on COD10K,our EGMFNet-P improves Fβby 4.8 points and reduces mean absolute error(MAE)by 0.006 compared with ZoomNeXt;on NC4K,it achieves a 3.6-point increase in Fβ.OnCAMO and CHAMELEON,it obtains 4.5-point increases in Fβ,respectively.These consistent gains substantiate the superiority and robustness of EGMFNet. 展开更多
关键词 Camouflaged object detection multi-scale feature fusion edge-guided image segmentation
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A Multi-Scale Feature Enhancement and Attention-Guided Network for Remote Sensing Change Detection 认领 引用
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作者 Junling Sun Chao Shu +3 位作者 Hongguang Wei Yi Yang Xinyue Zhang Pengge Ma 《Journal of Beijing Institute of Technology》 EI CAS 2026年第3期343-362,共20页
Remote sensing change detection(RSCD)plays a critical role in disaster assessment,land use monitoring,and environmental analysis.Despite notable progress with deep learning,especially convolutional neural networks(CNN... Remote sensing change detection(RSCD)plays a critical role in disaster assessment,land use monitoring,and environmental analysis.Despite notable progress with deep learning,especially convolutional neural networks(CNNS)and transformer-based models,existing approaches still face challenges such as false detections in multi-scale objects and cross-scale semantic inconsistencies.To address these limitations,we propose DMF-CDNet,a dual-stream multi-scale fusion change detection built on a resnet-18 backbone.The model integrates two key modules:the featureenhanced spatial-spectral feature coordination(FE-SSFC)module,which combines pyramid split attention module with residuals(PSAR)-based multi-scale convolution and statistically guided enhancement to highlight true changes and suppress noise,and the dual-branch decoding module(DDM),which incorporates guidance and channel rearrangement strategies to improve semantic consistency and boundary preservation.Through progressive decoding and cross-scale fusion,the network achieves more accurate localization of change regions and finer boundary recovery.Experimental results on the LEVIR-CD,HRCUS-CD,and SYSU-CD datasets demonstrate that DMFCDNet achieves F1-scores of 91.52%,74.92%,and 81.96%,respectively,confirming its effectiveness in multi-scale modeling and fine-grained change detection for complex RSCD scenarios. 展开更多
关键词 remote sensing images change detection multi-scale feature fusion spatial-spectral feature collaboration attention mechanism
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Improved multi-scale feature fusion for infrared small target detection based on YOLOv8 认领 引用
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作者 DING Shangsi YANG Guiqin GAN Bingkun 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2026年第2期208-218,共11页
Aiming at the problems of low target pixels and intricate background in small target detection in infrared scenes,a target detection model based on multi-scale feature extraction with YOLOv8 was proposed.Firstly,all d... Aiming at the problems of low target pixels and intricate background in small target detection in infrared scenes,a target detection model based on multi-scale feature extraction with YOLOv8 was proposed.Firstly,all downsampling convolutions in the network were replaced with the Haar wavelet downsampling(HWD)module to better preserve fine-grained details in infrared imagery during downsampling.Secondly,the spatial pyramid pooling-fast(SPPF)module was improved by introducing separable convolutions,which expanded the receptive field in both horizontal and vertical directions,enabling more comprehensive spatial information capture.Furthermore,a novel C2f_CDWR module was designed using dilated convolutions with varying dilation rates to achieve adaptive feature extraction across multiple receptive fields,thus enhancing detection performance for objects of different sizes.Finally,to improve localization accuracy,the original CIoU loss in YOLOv8 was replaced with Inner-SIoU,which effectively improved bounding box regression accuracy and significantly boosted the model’s capability in detecting small infrared targets.The experimental evaluation on the HIT-UAV dataset shows that the precision of the enhanced YOLOv8 model is 90.5%,the recall rate is 75.9%,and the mean average precision is 85.7%.In terms of infrared target detection,its performance was significantly better than that of the baseline YOLOv8 model and other benchmark models. 展开更多
关键词 infrared image small object detection multi-scale feature extraction dilation convolution YOLOv8v8
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BDMFuse:Multi-scale network fusion for infrared and visible images based on base and detail features 认领 引用 被引量：2
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作者 SI Hai-Ping ZHAO Wen-Rui +4 位作者 LI Ting-Ting LI Fei-Tao Fernando Bacao SUN Chang-Xia LI Yan-Ling 《红外与毫米波学报》 SCIE EI CAS CSCD 北大核心 2025年第2期289-298,共10页
The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method f... The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception. 展开更多
关键词 infrared image visible image image fusion encoder-decoder multi-scale features
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Fake News Detection Based on Cross-Modal Ambiguity Computation and Multi-Scale Feature Fusion 认领 引用 被引量：3
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作者 Jianxiang Cao Jinyang Wu +5 位作者 Wenqian Shang Chunhua Wang Kang Song Tong Yi Jiajun Cai Haibin Zhu 《Computers, Materials & Continua》 SCIE EI 2025年第5期2659-2675,共17页
With the rapid growth of socialmedia,the spread of fake news has become a growing problem,misleading the public and causing significant harm.As social media content is often composed of both images and text,the use of... With the rapid growth of socialmedia,the spread of fake news has become a growing problem,misleading the public and causing significant harm.As social media content is often composed of both images and text,the use of multimodal approaches for fake news detection has gained significant attention.To solve the problems existing in previous multi-modal fake news detection algorithms,such as insufficient feature extraction and insufficient use of semantic relations between modes,this paper proposes the MFFFND-Co(Multimodal Feature Fusion Fake News Detection with Co-Attention Block)model.First,the model deeply explores the textual content,image content,and frequency domain features.Then,it employs a Co-Attention mechanism for cross-modal fusion.Additionally,a semantic consistency detectionmodule is designed to quantify semantic deviations,thereby enhancing the performance of fake news detection.Experimentally verified on two commonly used datasets,Twitter and Weibo,the model achieved F1 scores of 90.0% and 94.0%,respectively,significantly outperforming the pre-modified MFFFND(Multimodal Feature Fusion Fake News Detection with Attention Block)model and surpassing other baseline models.This improves the accuracy of detecting fake information in artificial intelligence detection and engineering software detection. 展开更多
关键词 Fake news detection multimodal cross-modal ambiguity computation multi-scale feature fusion
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Joint Feature Encoding and Task Alignment Mechanism for Emotion-Cause Pair Extraction 认领 引用
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作者 Shi Li Didi Sun 《Computers, Materials & Continua》 SCIE EI 2025年第1期1069-1086,共18页
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions... With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings. 展开更多
关键词 Emotion-cause pair extraction interactive information enhancement joint feature encoding label consistency task alignment mechanisms
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Optimized Convolutional Neural Networks with Multi-Scale Pyramid Feature Integration for Efficient Traffic Light Detection in Intelligent Transportation Systems 认领 引用 被引量：1
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作者 Yahia Said Yahya Alassaf +2 位作者 Refka Ghodhbani Taoufik Saidani Olfa Ben Rhaiem 《Computers, Materials & Continua》 SCIE EI 2025年第2期3005-3018,共14页
Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportatio... Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks. 展开更多
关键词 Intelligent transportation systems(ITS) traffic light detection multi-scale pyramid feature maps advanced driver assistance systems(ADAS) real-time detection AI in transportation
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AMSFuse:Adaptive Multi-Scale Feature Fusion Network for Diabetic Retinopathy Classification 认领 引用
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作者 Chengzhang Zhu Ahmed Alasri +5 位作者 Tao Xu Yalong Xiao Abdulrahman Noman Raeed Alsabri Xuanchu Duan Monir Abdullah 《Computers, Materials & Continua》 SCIE EI 2025年第3期5153-5167,共15页
Globally,diabetic retinopathy(DR)is the primary cause of blindness,affecting millions of people worldwide.This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure p... Globally,diabetic retinopathy(DR)is the primary cause of blindness,affecting millions of people worldwide.This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective treatment.Deep learning-based automated diagnosis for diabetic retinopathy can facilitate early detection and treatment.However,traditional deep learning models that focus on local views often learn feature representations that are less discriminative at the semantic level.On the other hand,models that focus on global semantic-level information might overlook critical,subtle local pathological features.To address this issue,we propose an adaptive multi-scale feature fusion network called(AMSFuse),which can adaptively combine multi-scale global and local features without compromising their individual representation.Specifically,our model incorporates global features for extracting high-level contextual information from retinal images.Concurrently,local features capture fine-grained details,such as microaneurysms,hemorrhages,and exudates,which are critical for DR diagnosis.These global and local features are adaptively fused using a fusion block,followed by an Integrated Attention Mechanism(IAM)that refines the fused features by emphasizing relevant regions,thereby enhancing classification accuracy for DR classification.Our model achieves 86.3%accuracy on the APTOS dataset and 96.6%RFMiD,both of which are comparable to state-of-the-art methods. 展开更多
关键词 Diabetic retinopathy multi-scale feature fusion global features local features integrated attention mechanism retinal images
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Multi-Scale Feature Fusion Network for Accurate Detection of Cervical Abnormal Cells 认领 引用
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作者 Chuanyun Xu Die Hu +3 位作者 Yang Zhang Shuaiye Huang Yisha Sun Gang Li 《Computers, Materials & Continua》 SCIE EI 2025年第4期559-574,共16页
Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells an... Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size.Pathologists often refer to surrounding cells to identify abnormalities.To emulate this slide examination behavior,this study proposes a Multi-Scale Feature Fusion Network(MSFF-Net)for detecting cervical abnormal cells.MSFF-Net employs a Cross-Scale Pooling Model(CSPM)to effectively capture diverse features and contextual information,ranging from local details to the overall structure.Additionally,a Multi-Scale Fusion Attention(MSFA)module is introduced to mitigate the impact of cell size variations by adaptively fusing local and global information at different scales.To handle the complex environment of cervical cell images,such as cell adhesion and overlapping,the Inner-CIoU loss function is utilized to more precisely measure the overlap between bounding boxes,thereby improving detection accuracy in such scenarios.Experimental results on the Comparison detector dataset demonstrate that MSFF-Net achieves a mean average precision(mAP)of 63.2%,outperforming state-of-the-art methods while maintaining a relatively small number of parameters(26.8 M).This study highlights the effectiveness of multi-scale feature fusion in enhancing the detection of cervical abnormal cells,contributing to more accurate and efficient cervical cancer screening. 展开更多
关键词 Cervical abnormal cells image detection multi-scale feature fusion contextual information
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MSFResNet:A ResNeXt50 model based on multi-scale feature fusion for wild mushroom identification 认领 引用
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作者 YANG Yang JU Tao +1 位作者 YANG Wenjie ZHAO Yuyang 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2025年第1期66-74,共9页
To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network mo... To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network model is proposed by fusing multi-scale feature information.Firstly,a multi-scale feature extraction module is designed to obtain multi-scale information on feature images by using different scales of convolution kernels.Meanwhile,the channel attention mechanism is used to increase the global information acquisition of the network.Secondly,the feature images processed by the multi-scale feature extraction module are fused with the deep feature images through short links to guide the full learning of the network,thus reducing the loss of texture details of the deep network feature images,and improving network generalization ability and recognition accuracy.Finally,the validity of the MSFResNet model is verified using public datasets and applied to wild mushroom identification.Experimental results show that compared with ResNeXt50 network model,the accuracy of the MSFResNet model is improved by 6.01%on the FGVC-Aircraft common dataset.It achieves 99.13%classification accuracy on the wild mushroom dataset,which is 0.47%higher than ResNeXt50.Furthermore,the experimental results of the thermal map show that the MSFResNet model significantly reduces the interference of background information,making the network focus on the location of the main body of wild mushroom,which can effectively improve the accuracy of wild mushroom identification. 展开更多
关键词 multi-scale feature fusion attention mechanism ResNeXt50 wild mushroom identification deep learning
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WiFi-Based Indoor Intrusion Detection via Two-Level Gait Feature Fusion Model 认领 引用
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作者 Lijun Cui Yongjie Niu +3 位作者 Yuxiang Sun Xiaokang Gu Jing Guo Pengfei Xu 《Computers, Materials & Continua》 SCIE EI 2026年第7期370-387,共18页
Indoor intrusion detection is essential for various applications,including security systems and smart homes.Recently,WiFi-based detection has gained popularity due to its low cost and non-invasive nature.Current Chann... Indoor intrusion detection is essential for various applications,including security systems and smart homes.Recently,WiFi-based detection has gained popularity due to its low cost and non-invasive nature.Current Channel State Information(CSI)based frameworks primarily use deep learning to extract gait signatures;however,their performance depends heavily on extensive labeled datasets.These methods struggle to differentiate between unlabeled and labeled data that exhibit similar features.To address this challenge,we propose a novel Two-level Feature Fusion model for Indoor Intrusion Detection(TFF-IID)utilizing commercial WiFi CSI.The model adopts a two-level structure to learn rich feature representations and introduces a Transformer with multi-head self-attention alongside a multi-scale convolution module to process sensor data.Additionally,it incorporates a self-supervised learning module to capture general normality patterns.Based on this architecture,TFF-IID achieves accurate intrusion detection using only CSI.Empirical evaluations on a private gait dataset demonstrate that TFF-IID achieves an intrusion detection accuracy of 73.5%and an F1-score of 76.2%across 10 unauthorized subjects.Moreover,cross-scenario assessments verify that the proposed model maintains high efficiency and robustness in environments characterized by diverse spatial layouts and multipath complexities.Furthermore,TFF-IID outperforms the best baseline by 19.7%and 25.7%in accuracy and F1-score,respectively. 展开更多
关键词 Intrusion detection transformer feature fusion WIFI signal CSI multi-scale convolution module
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Dual-Strategy Improvement of YOLOv11n for Multi-Scale Object Detection in Remote Sensing Images 认领 引用
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作者 Shuaiyu Zhu Sergey Ablameyko Ji Li 《Computers, Materials & Continua》 SCIE EI 2026年第8期1382-1398,共17页
Satellite remote sensing images pose significant challenges for object detection due to their high resolution,complex scenes,and large variations in target scales.To address the insufficient detection accuracy of the ... Satellite remote sensing images pose significant challenges for object detection due to their high resolution,complex scenes,and large variations in target scales.To address the insufficient detection accuracy of the YOLOv11n model in remote sensing imagery,this paper proposes two improvement strategies.Method 1:(a)a Large Separable Kernel Attention(LSKA)mechanism is introduced into the backbone network to enhance feature extraction for small objects;(b)a Gold-YOLO structure is incorporated into the neck network to achieve multi-scale feature fusion,thereby improving the detection performance of objects at different scales.Method 2:(a)the Gold-YOLO structure is also integrated into the neck network;(b)a MultiSEAMHead detection head is combined to further strengthen the representation and detection capability for small and multi-scale objects.To verify the effectiveness of the proposed improvements,experiments are conducted on the DOTAv1 dataset.The results show that,while maintaining the lightweight advantage of the model,the proposed methods improve detection accuracy(mAP@0.5)by 1.3%and 1.8%,respectively,compared with the baseline YOLOv11n,demonstrating the effectiveness and practical value of the proposed approaches for object detection in remote sensing images. 展开更多
关键词 Remote sensing imagery YOLOv11n multi-scale object detection lightweight deep learning attention mechanism feature fusion
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PointNMSA: An Improved PointNeXt Network with Non-Local Multi-Scale Aggregation for 3D Point Cloud Semantic Segmentation 认领 引用
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作者 Aihua Wu Chenlu Huang 《Computers, Materials & Continua》 SCIE EI 2026年第8期1632-1649,共18页
Three-dimensional(3D)point cloud semantic segmentation is a core task in indoor scene understanding,providing detailed semantic information about spatial structures and object categories in indoor environments.Althoug... Three-dimensional(3D)point cloud semantic segmentation is a core task in indoor scene understanding,providing detailed semantic information about spatial structures and object categories in indoor environments.Although methods based on deep learning have made steady progress in recent years,accurately segmenting complex indoor scenes remains challenging due to the unordered nature of point clouds and variations across large scales.Most existing networks have limited capability for multi-scale feature aggregation and struggle to balance local geometric details with global semantic context.These issues are further exacerbated by hierarchical downsampling,which often leads to the loss of fine-grained structural information.Moreover,feature interaction restricted to local neighborhoods may limit the capture of non-local semantic dependencies in complex indoor scenes.To address these limitations,we propose PointNMSA(PointNeXt with Non-local Multi-Scale Aggregation),an improved semantic segmentation network built upon the PointNeXt backbone.A Multi-Scale Feature Enhancement(MSFE)module is introduced in the decoding stage to fuse features from different encoding levels,and further refines the fused features to produce more stable multi-scale representations,which preserves geometric details across scales.In addition,a Convolution-Attention Mixing(CA-Mix)module is designed to jointly integrate local spatial structures and non-local contextual dependencies via dual-stream aggregation and multi-dimensional attention fusion,thereby enabling more discriminative feature representations.Experiments on the Stanford Large-Scale 3D Indoor Spaces(S3DIS)benchmark demonstrate the effectiveness of PointNMSA.On the Area 5 test split,PointNMSA achieves a mean intersection over union(mIoU)of 65.10%,outperforming the PointNeXt baseline by 1.59%,while introducing only a modest increase in computational cost(latency from 42.24 to 45.18 ms and parameters from 3.16 to 8.67M).Despite the noticeable growth in parameter count,the increase in inference latency remains relatively limited,indicating a favorable trade-off between segmentation accuracy and computational efficiency.Additional cross-dataset experiments on ScanNet further verify that PointNMSA maintains stable gains under different indoor scene distributions.Such performance gains suggest that PointNMSA provides a more robust and generalizable solution for semantic segmentation in large-scale indoor environments with complex structural layouts. 展开更多
关键词 3D point cloud semantic segmentation indoor scene understanding multi-scale feature aggregation non-local context integration PointNeXt
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YOLO-SPDNet:Multi-Scale Sequence and Attention-Based Tomato Leaf Disease Detection Model 认领 引用 被引量：1
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作者 Meng Wang Jinghan Cai +6 位作者 Wenzheng Liu Xue Yang Jingjing Zhang Qiangmin Zhou Fanzhen Wang Hang Zhang Tonghai Liu 《Phyton-International Journal of Experimental Botany》 SCIE 2026年第1期290-308,共19页
Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet th... Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet the requirements of early disease identification in complex natural environments.To address this issue,this study proposes an improved YOLO11-based model,YOLO-SPDNet(Scale Sequence Fusion,Position-Channel Attention,and Dual Enhancement Network).The model integrates the SEAM(Self-Ensembling Attention Mechanism)semantic enhancement module,the MLCA(Mixed Local Channel Attention)lightweight attention mechanism,and the SPA(Scale-Position-Detail Awareness)module composed of SSFF(Scale Sequence Feature Fusion),TFE(Triple Feature Encoding),and CPAM(Channel and Position Attention Mechanism).These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness.Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%,a recall of 86.5%,and an mAP@0.5 of 90.6%on the test set,with a computational complexity of 12.5 GFLOPs.Furthermore,the model reaches a real-time inference speed of 987 FPS,making it suitable for deployment on mobile agricultural terminals and online monitoring systems.Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes. 展开更多
关键词 Tomato disease detection YOLO multi-scale feature fusion attention mechanism lightweight model
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