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YOLO-SDD:An Improved YOLOv5 for Storm Drain Detection in Street-Level View 认领 引用
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作者 WANG Jing FANG Zhiqiang +4 位作者 LI Qianqian TANG Zhiwei HUANG Zhangyang HONG Zhonghua HE Haiyang 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期359-374,共16页
Urban drainage pipe system is an important part of city management.Automated detection of the status of storm drain in street-level images through current technologies in computer vision and AI is an important aspect ... Urban drainage pipe system is an important part of city management.Automated detection of the status of storm drain in street-level images through current technologies in computer vision and AI is an important aspect of smart city construction.In this paper,a framework based on YOLOv5s for storm drain detection(YOLOSDD)in street view is proposed.By analyzing the characteristics of small-scale targets,YOLO-SDD focuses on optimizing the Backbone network and its loss function.Series of experiments demonstrated that in the task of detecting different states of storm drain under various environmental conditions,the mean average precision(mAP@.5)of the YOLO-SDD can reach 89.6%,increasing by 2%compared with the baseline model YOLOv5s.In the presence and absence of occlusion,the average precision of storm drain detection increased by 0.9%and 3.1%,respectively.In addition,the effectiveness and generalization ability of YOLO-SDD were further validated using the storm drain dataset of Urbana-Champaign(SDUC)from Illinois,USA,and the dataset for object detection in aerial images(DOTA).Finally,this work has deployed the YOLO-SDD on the Android system,which verifies its ability of real-time detecting storm drain in different states in street scenes. 展开更多
关键词 storm drain detection YOLOv5s selective kernel attention spatial pyramid pooling cross stage partial connection SCYLLA-IoU
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An improved YOLOv8 apple leaf disease detection algorithm 认领 引用
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作者 Xinyu PEI Wei YUAN +1 位作者 Yuexiu ZHANG Lianjun SONG 《Optoelectronics Letters》 EI 2026年第5期314-320,共7页
This paper suggests an improved you only look once version 8n(YOLOv8n)algorithm for apple leaf disease detection,abbreviated as ALWB-YOLOv8n.The model is comprised of four essential components.Initially,arbitrary kern... This paper suggests an improved you only look once version 8n(YOLOv8n)algorithm for apple leaf disease detection,abbreviated as ALWB-YOLOv8n.The model is comprised of four essential components.Initially,arbitrary kernel convolution(AKConv)replaces the convolution module,which significantly decreases both the model’s parameter count and its overall size.Secondly,the large selective kernel network(LSKNet)attention mechanism is added in the Backbone,which can dynamically adjust the spatial sensory domain,and experiments have proved that this method is extremely advantageous for small target detection.Third,a weighted bi-directional feature pyramid network is introduced,which enables the model to achieve multi-scale feature fusion and is more concise and faster.Finally,wise intersection over union(WIoU)is used to replace complete intersection over union(CIoU)in YOLOv8,and the idea of focal loss is introduced,which effectively solves the detection problems in cases such as apple leaves occluding each other and blurred boundaries of diseased leaves.The improved algorithm exhibits superior performance compared to other common object detection algorithms.Compared with YOLOv8n,the improved algorithm achieves 2.3%improvement in precision,3.8%improvement in recall,and 2.5%and 2.7%improvement in mAP0.5 and mAP0.5:0.95,respectively.Compared with YOLOv8n,the improved model reduces the number of parameters and size of the model and realizes real-time monitoring with a frames per second(FPS)of 50.5. 展开更多
关键词 large selective kernel network arbitrary kernel convolution large selective kernel network lsknet attention kernel convolution akconv replaces weighted bi directional feature pyramid network adjust spatial sensory domainand improved YOLOv n algorithm apple leaf disease detection
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融合SKNet与MobilenetV3的芒果叶片病虫害分类方法 认领 引用 被引量：2
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作者 沈熠辉 何惠彬 +1 位作者 陈小宇 颜胜男 《福建农业学报》 CAS CSCD 北大核心 2024年第5期584-592,共9页
【目的】针对芒果叶片病虫害缺少数据集和识别准确率低的问题,筛选构建芒果叶片病虫害分类模型,以提高芒果叶病虫害分类准确率。【方法】提出使用去噪扩散模型进行病虫害数据增强,同时联合SKNet与MobilenetV3模型的芒果叶片病虫害分类... 【目的】针对芒果叶片病虫害缺少数据集和识别准确率低的问题,筛选构建芒果叶片病虫害分类模型,以提高芒果叶病虫害分类准确率。【方法】提出使用去噪扩散模型进行病虫害数据增强,同时联合SKNet与MobilenetV3模型的芒果叶片病虫害分类方法。首先使用去噪扩散模型对数据集进行扩充,再采用多尺度结构相似性指标对生成的病虫害图像与拍摄的病虫害图像之间的相似程度进行评估,接着对DDIM与DCGAN网络训练和生成效果进行比对。在MobilenetV3模型中,将SE注意力模块替换为SKNet模块进行构建网络模型。【结果】使用DDIM生成的所有类型的病虫害图像与拍摄的病虫害图像的MS-SSIM指标均大于0.63,且都高于DCGAN。相较于其他注意力模块,联合SKNet与MobilenetV3的分类效果最佳,在98%以上。对添加CA、CBAM、ECA注意力模块进行平滑类激活图可视化,对比其他注意力模块,使用SKNet注意力分布区域更为集中在病虫害叶片上。【结论】该方法在病虫害叶片检测上具有良好的应用前景,能提升病虫害识别效率与精度,减少检测成本,同时可应用于移动式或者嵌入式设备。 展开更多
关键词 芒果叶片 扩散概率模型 Mobilenet Selective Kernel Networks
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Selective ensemble modeling based on nonlinear frequency spectral feature extraction for predicting load parameter in ball mills 认领 引用 被引量：5
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作者 汤健 柴天佑 +1 位作者 刘卓 余文 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2015年第12期2020-2028,共9页
Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model ... Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones. 展开更多
关键词 Nonlinear latent feature extraction Kernel partial least squares Selective ensemble modeling Least squares support vector machines Material to ball volume ratio
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Deep neural network based on multi-level wavelet and attention for structured illumination microscopy 认领 引用
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作者 Yanwei Zhang Song Lang +2 位作者 Xuan Cao Hanqing Zheng Yan Gong 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2024年第2期12-23,共12页
Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior know... Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems. 展开更多
关键词 Super-resolution reconstruction multi-level wavelet packet transform residual channel attention selective kernel attention
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基于SKNet的增强型Pix2pixHD图像去雾方法 认领 引用 被引量：2
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作者 段雅童 许光宇 《湖北理工学院学报》 2022年第4期23-28,共6页
针对采用去雾算法处理后图片仍存在色彩失真、残雾遗留等问题,提出一种基于SKNet的增强型Pix2pixHD去雾方法,即在增强型Pix2pixHD网络的增强模块中引入SKNet网络,实现了不同尺度特征的选择和融合,提高了算法对图像特征的利用率。此外,在... 针对采用去雾算法处理后图片仍存在色彩失真、残雾遗留等问题,提出一种基于SKNet的增强型Pix2pixHD去雾方法,即在增强型Pix2pixHD网络的增强模块中引入SKNet网络,实现了不同尺度特征的选择和融合,提高了算法对图像特征的利用率。此外,在SKNet网络前添加可变形卷积,使得卷积核产生自由形变,以适应不规则的目标物体,增强了模型的可变换能力,达到了很好的去雾效果。较现有的去雾方法,文章提出的网络更好地利用了图像各个尺度的特征细节,提高了图像去雾的效果。 展开更多
关键词 图像去雾 Pix2pixHD Selective Kernel Network(SKNet) 可变形卷积
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Joint Rain Streaks & Haze Removal Network for Object Detection 认领 引用
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作者 Ragini Thatikonda Prakash Kodali +1 位作者 Ramalingaswamy Cheruku Eswaramoorthy K.V 《Computers, Materials & Continua》 SCIE EI 2024年第6期4683-4702,共20页
In the realm of low-level vision tasks,such as image deraining and dehazing,restoring images distorted by adverse weather conditions remains a significant challenge.The emergence of abundant computational resources ha... In the realm of low-level vision tasks,such as image deraining and dehazing,restoring images distorted by adverse weather conditions remains a significant challenge.The emergence of abundant computational resources has driven the dominance of deep Convolutional Neural Networks(CNNs),supplanting traditional methods reliant on prior knowledge.However,the evolution of CNN architectures has tended towards increasing complexity,utilizing intricate structures to enhance performance,often at the expense of computational efficiency.In response,we propose the Selective Kernel Dense Residual M-shaped Network(SKDRMNet),a flexible solution adept at balancing computational efficiency with network accuracy.A key innovation is the incorporation of an M-shaped hierarchical structure,derived from the U-Net framework as M-Network(M-Net),within which the Selective Kernel Dense Residual Module(SDRM)is introduced to reinforce multi-scale semantic feature maps.Our methodology employs two sampling techniques-bilinear and pixel unshuffled and utilizes a multi-scale feature fusion approach to distil more robust spatial feature map information.During the reconstruction phase,feature maps of varying resolutions are seamlessly integrated,and the extracted features are effectively merged using the Selective Kernel Fusion Module(SKFM).Empirical results demonstrate the comprehensive superiority of SKDRMNet across both synthetic and real rain and haze datasets. 展开更多
关键词 Image deraining Selective Dense Residual Module(SDRM) Selective Kernel Fusion Module(SKFM) Selective KernelDense Residual M-Shaped Network(SKDRMNet)
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A selective kernel-based cycle-consistent generative adversarial network for unpaired low-dose CT denoising 认领 引用 被引量：2
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作者 Chaoqun Tan Mingming Yang +2 位作者 Zhisheng You Hu Chen Yi Zhang 《Precision Clinical Medicine》 2022年第2期125-136,共12页
Low-dose computed tomography(LDCT)denoising is an indispensable procedure in the medical imaging field,which not only improves image quality,but can mitigate the potential hazard to patients caused by routine doses.De... Low-dose computed tomography(LDCT)denoising is an indispensable procedure in the medical imaging field,which not only improves image quality,but can mitigate the potential hazard to patients caused by routine doses.Despite the improvement in performance of the cycle-consistent generative adversarial network(CycleGAN)due to the well-paired CT images shortage,there is still a need to further reduce image noise while retaining detailed features.Inspired by the residual encoder–decoder convolutional neural network(RED-CNN)and U-Net,we propose a novel unsupervised model using CycleGAN for LDCT imaging,which injects a two-sided network into selective kernel networks(SK-NET)to adaptively select features,and uses the patchGAN discriminator to generate CT images with more detail maintenance,aided by added perceptual loss.Based on patch-based training,the experimental results demonstrated that the proposed SKFCycleGAN outperforms competing methods in both a clinical dataset and the Mayo dataset.The main advantages of our method lie in noise suppression and edge preservation. 展开更多
关键词 cycle-consistent adversarial network selective kernel networks unsupervised low dose CT image denoising clinical dataset
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