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DDFNet:real-time salient object detection with dual-branch decoding fusion for steel plate surface defects 认领 引用 被引量：1
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作者 Tao Wang Wang-zhe Du +5 位作者 Xu-wei Li Hua-xin Liu Yuan-ming Liu Xiao-miao Niu Ya-xing Liu Tao Wang 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2025年第8期2421-2433,共13页
A novel dual-branch decoding fusion convolutional neural network model(DDFNet)specifically designed for real-time salient object detection(SOD)on steel surfaces is proposed.DDFNet is based on a standard encoder–decod... A novel dual-branch decoding fusion convolutional neural network model(DDFNet)specifically designed for real-time salient object detection(SOD)on steel surfaces is proposed.DDFNet is based on a standard encoder–decoder architecture.DDFNet integrates three key innovations:first,we introduce a novel,lightweight multi-scale progressive aggregation residual network that effectively suppresses background interference and refines defect details,enabling efficient salient feature extraction.Then,we propose an innovative dual-branch decoding fusion structure,comprising the refined defect representation branch and the enhanced defect representation branch,which enhance accuracy in defect region identification and feature representation.Additionally,to further improve the detection of small and complex defects,we incorporate a multi-scale attention fusion module.Experimental results on the public ESDIs-SOD dataset show that DDFNet,with only 3.69 million parameters,achieves detection performance comparable to current state-of-the-art models,demonstrating its potential for real-time industrial applications.Furthermore,our DDFNet-L variant consistently outperforms leading methods in detection performance.The code is available at http://gffzz188fe103f8f1460as5pcwpcvv9x9w6qv9.ffgz.tsg.suse.edu.cn/13140W/DDFNet. 展开更多
关键词 Steel plate surface defect Real-time detection Salient object detection Dual-branch decoder Multi-scale attention fusion Multi-scale residual fusion
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GCRA-FWVAE:Anomaly detection for IIoT univariate time series using time-frequency domain analysis 认领 引用 被引量：1
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作者 Xiaoling Tao Haowei Liu +3 位作者 Wenbo Zhao Weikun Li Yaqi Nie Jingqi Fu 《Digital Communications and Networks》 SCIE EI CSCD 2026年第3期405-416,共12页
With the rapid development of Artificial Intelligence of Things(AIoT)technologies,the security of Industrial Internet of Things(IIoT)data faces increasing challenges,particularly in time series anomaly detection.IIoT ... With the rapid development of Artificial Intelligence of Things(AIoT)technologies,the security of Industrial Internet of Things(IIoT)data faces increasing challenges,particularly in time series anomaly detection.IIoT data are typically scarce in abnormal samples and noisy,making unsupervised learning a common solution.The security challenges of IIoT data in AIoT environments require robust unsupervised anomaly detection methods.While Variational Autoencoders(VAEs)excel in noise resilience,they face two critical challenges in IIoT data:difficulties in single-variable time-series modeling and conflicts between static prior assumptions and dynamic temporal features.To address these challenges,we propose the Greater Cane Rat Algorithm-enhanced FourierWavelet Conditional Variational Autoencoder(GCRA-FWVAE).Our method introduces a time-frequency dualbranch architecture that synergistically combines wavelet transforms for localized transient feature extraction and Fourier transforms for global spectral characterization.These complementary representations jointly regulate the Conditional Variational Autoencoder(CVAE)reconstruction process,effectively preserving critical anomaly signatures while suppressing noise interference.The architecture is further optimized through bioinspired Greater Cane Rat Algorithm(GCRA)to improve adaptive learning capabilities.Extensive validation on the Yahoo benchmark indicates state-of-the-art performance,achieving an F1-score of 93.6%(an improvement of 4.5% over baseline VAEs)and a precision of 95.1%.These improvements significantly increase anomaly detection accuracy and robustness,particularly in the AIoT environment,where it effectively handles more complex and dynamic industrial data. 展开更多
关键词 Anomaly detection Time series GCRA CVAE Time-frequency domain
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SAA-O2DINO:Oriented object detection transformer with improved denoising anchor boxes and shape-adaptive assigner 认领 引用 被引量：2
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作者 Ruijiao JIN Kun WANG +2 位作者 Zhang LI Xichao TENG Minhao LIU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第5期547-560,共14页
In contrast to the nearly fixed flying altitude of satellite remote sensing platforms,aerial remote sensing(e.g.,unmanned aerial vehicles)often employs oblique photography at varying flying altitudes to observe object... In contrast to the nearly fixed flying altitude of satellite remote sensing platforms,aerial remote sensing(e.g.,unmanned aerial vehicles)often employs oblique photography at varying flying altitudes to observe objects from multiple angles and distances in real time.While the existing oriented object detection methods have already demonstrated reliable results in most satellite remote sensing scenarios and achieved high detection precision on large public datasets,such as DOTA-v1.0 and DIOR-R,these methods tend to perform suboptimally on aerial remote sensing images.This performance gap is primarily due to the following two challenges:(A)significant shape variation of objects under multi-view imaging scenarios and(B)substantial object scale variation under multi-distance imaging conditions.To address these issues,we propose the SAA-O2DINO(oriented object detection transformer with improved denoising anchor boxes and shape-adaptive assigner)method for aerial remote sensing in this paper.The proposed method is based on the recently developed AO2DINO framework.It introduces an enhanced Shape-Adaptive Assigner(SAA)that incorporates object shape information into the threshold estimation,allowing for more accurate separation of positive and negative samples,thereby improving the model's adaptability to significant shape changes across different imaging angles.Additionally,a Gradient Calibration Loss(GCL)is introduced to mitigate the problem of object scale variation.The GCL employs a gradient scaling strategy to reduce scale sensitivity during the optimisation process.We comprehensively compare the proposed method against typical oriented object detection approaches on the DOTA-v1.0 and VSAI datasets.The results show that the proposed method has substantial improvement in detection performance across all datasets,particularly for aerial remote sensing images,validating the generalisation capabilities of our model. 展开更多
关键词 Aerial remote sensing imagery DETR Oriented Object Detection Rotate IoU loss Shape-adaptive assigner
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Algorithm improvement for traffic sign detection based on YOLOv8 认领 引用 被引量：1
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作者 Dongmei MA Xuelong LYU Qirong ZHU 《Optoelectronics Letters》 EI 2026年第2期92-97,共6页
An improved algorithm for traffic sign detection based on YOLOv8 is proposed. Firstly, YOLOv8n is used as the base model of the network, the inverted residual mobile block and exponential moving average(iRMB_EMA) atte... An improved algorithm for traffic sign detection based on YOLOv8 is proposed. Firstly, YOLOv8n is used as the base model of the network, the inverted residual mobile block and exponential moving average(iRMB_EMA) attention mechanism is used to improve the model's ability to perceive small targets, which reduces the leakage detection phenomenon of the model, convolution(Conv) is upgraded to receptive-field attention convolution(RFAConv), which improves the model's ability to deal with details and complexity in the image, the idea of adaptive spatial feature fusion(ASFF) is introduced in the detection head, and the small target detection layer, a four-head detection head is designed to improve the model's ability to detect small targets, solves the case of feature loss due to cross-scale fusion, and use of the Inner-minimum points distance intersection over union(MPDIoU) loss function provides a more accurate loss metric by calculating the distance of key points between the predicted and true frames. The experimental results of this algorithm on the public dataset CCTSDB show that the improved model mean average precision(m AP) reaches 82.6%, which is 4% higher than the YOLOv8n. The experimental results of dataset TT100k show that the m AP reaches 84.5%, which is 7.1% higher than the YOLOv8n. This algorithm effectively improves the problem of detail perception and leakage of the model in the detection of small targets, and has a significant detection effect compared to other algorithms. 展开更多
关键词 inverted residual mobile block traffic sign detection perceive small targets leakage detection phenomenon YOLOv base model deal details
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Steel Surface Defect Detection via the Multiscale Edge Enhancement Method 认领 引用 被引量：1
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作者 Yuanyuan Wang Yemeng Zhu +2 位作者 Xiuchuan Chen Tongtong Yin Shiwei Su 《Computers, Materials & Continua》 SCIE EI 2026年第3期1006-1032,共27页
To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects,similar defects and background features,and similarities between differ... To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects,similar defects and background features,and similarities between different defects,this paper proposes a lightweight detection model named multiscale edge and squeeze-and-excitation attention detection network(MSESE),which is built upon the You Only Look Once version 11 nano(YOLOv11n).To address the difficulty of locating defect edges,we first propose an edge enhancement module(EEM),apply it to the process of multiscale feature extraction,and then propose a multiscale edge enhancement module(MSEEM).By obtaining defect features from different scales and enhancing their edge contours,the module uses the dual-domain selection mechanism to effectively focus on the important areas in the image to ensure that the feature images have richer information and clearer contour features.By fusing the squeeze-and-excitation attention mechanism with the EEM,we obtain a lighter module that can enhance the representation of edge features,which is named the edge enhancement module with squeeze-and-excitation attention(EEMSE).This module was subsequently integrated into the detection head.The enhanced detection head achieves improved edge feature enhancement with reduced computational overhead,while effectively adjusting channel-wise importance and further refining feature representation.Experiments on the NEU-DET dataset show that,compared with the original YOLOv11n,the improved model achieves improvements of 4.1%and 2.2%in terms of mAP@0.5 and mAP@0.5:0.95,respectively,and the GFLOPs value decreases from the original value of 6.4 to 6.2.Furthermore,when compared to current mainstream models,Mamba-YOLOT and RTDETR-R34,our method achieves superior performance with 6.5%and 8.9%higher mAP@0.5,respectively,while maintaining a more compact parameter footprint.These results collectively validate the effectiveness and efficiency of our proposed approach. 展开更多
关键词 Steel defects object detection algorithms small target multiscale attention mechanism
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Dual-Signal Colorimetric/Fluorescent Detection of Vibrio parahaemolyticus in Seafood Using a Multifunctional Aptamer-Conjugated Magnetic Covalent Organic Framework-CuO/Au Nanozyme 认领 引用 被引量：1
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作者 SUN Di YANG Xuewen +6 位作者 WANG Hui LIN Hongyong HE Xiaoxia HUO Zhenting LIU Yu YU Zhongjie JIANG Wei 《食品科学》 EI CAS CSCD 北大核心 2026年第6期23-40,共18页
In this study,a multifunctional aptamer-conjugated magnetic covalent organic framework(COF)-CuO/Au nanozyme(MCOF-CuO/Au@apt)was developed as a“three-in-one”platform for dual-signal colorimetric and fluorescent detec... In this study,a multifunctional aptamer-conjugated magnetic covalent organic framework(COF)-CuO/Au nanozyme(MCOF-CuO/Au@apt)was developed as a“three-in-one”platform for dual-signal colorimetric and fluorescent detection of Vibrio parahaemolyticus.The nanozyme integrated magnetic separation,peroxidase-like catalytic activity,and specific target recognition through an aptamer-based strategy.Upon binding to V.parahaemolyticus,the catalytic oxidation of tetra-aminophenylethylene(TPE-4A)by the nanozyme was selectively inhibited,resulting in distinct colorimetric and fluorescent signals that significantly enhanced the detection accuracy and reliability.The proposed method exhibited high sensitivity,with limits of detection(LOD)of 21 and 7 CFU/mL for the colorimetric and fluorescent assays,respectively.The performance of this method was validated using real seafood samples,including Penaeus vannamei,Mytilus coruscus,and Crassostrea gigas,which showed high recovery rates(101.11%-107.30%)and excellent reproducibility.The system also demonstrated strong specificity and accuracy under various conditions,confirming its robustness and practical applicability.Collectively,this innovative platform presents a promising solution for the rapid,versatile,and sensitive detection of V.parahaemolyticus in seafood,with considerable potential to advance food safety diagnosis and on-site monitoring. 展开更多
关键词 Vibrio parahaemolyticus dual-signal detection aptamer-based nanozyme magnetic covalent organic framework seafood safety
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Embedding of ripening topology into one-stage detection for tomato cluster phenotyping 认领 引用 被引量：1
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作者 Bingquan CHU Ruiyuan WU +4 位作者 Haijun ZHANG Haochuan QIN Zishun PENG Fengle ZHU Yong HE 《Journal of Zhejiang University-SCIENCE B》 SCIE CAS CSCD 2026年第5期466-481,I0003-I0006,共16页
The automated assessment of tomato ripeness is vital for modern greenhouse operations,yet challenges remain due to variable environmental conditions.To provide a solution,we propose rank-aware You Only Look Once(YOLO)... The automated assessment of tomato ripeness is vital for modern greenhouse operations,yet challenges remain due to variable environmental conditions.To provide a solution,we propose rank-aware You Only Look Once(YOLO),a novel detection framework that incorporates the biological prior of top-to-bottom ripening within fruit clusters.This is achieved through two key innovations:an efficient position-aware head for regressing relative height for fruits and a dynamic margin-aware ranking loss(DM-RankLoss)that enforces the correct spatial sequence.Evaluated on a 3500-image dataset from a solar greenhouse,our plug-and-play module could boost the mean average precision(mAP)at intersection over union(IoU)threshold of 0.50(mAP50)of multiple YOLO architectures by up to 5.66 pecentage points.The model effectively learns the cluster topology,achieving a height-mean absolute error(H-MAE)of 0.107(normalized)and a pairwise ranking accuracy(PRA)of 84.59%,while it reduces the parameter count by over 10%compared to the baseline for efficient deployment.Visualizations confirm that the model leverages spatial context to resolve color ambiguities.Our work offers a sensor-free,accurate,and efficient solution for in situ phenotyping in agricultural robotics. 展开更多
关键词 Tomato ripeness Phenotype Object detection Topology You Only Look Once(YOLO) Spatial sequence
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Visual Detection Algorithms for Counter-UAV in Low-Altitude Air Defense 认领 引用 被引量：1
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作者 Minghui Li Hongbo Li +1 位作者 Jiaqi Zhu Xupeng Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第3期823-844,共22页
To address the challenge of real-time detection of unauthorized drone intrusions in complex low-altitude urban environments such as parks and airports,this paper proposes an enhanced MBS-YOLO(Multi-Branch Small Target... To address the challenge of real-time detection of unauthorized drone intrusions in complex low-altitude urban environments such as parks and airports,this paper proposes an enhanced MBS-YOLO(Multi-Branch Small Target Detection YOLO)model for anti-drone object detection,based on the YOLOv8 architecture.To overcome the limitations of existing methods in detecting small objects within complex backgrounds,we designed a C2f-Pu module with excellent feature extraction capability and a more compact parameter set,aiming to reduce the model’s computational complexity.To improve multi-scale feature fusion,we construct a Multi-Branch Feature Pyramid Network(MB-FPN)that employs a cross-level feature fusion strategy to enhance the model’s representation of small objects.Additionally,a shared detail-enhanced detection head is introduced to address the large size variations of Unmanned Aerial Vehicle(UAV)targets,thereby improving detection performance across different scales.Experimental results demonstrate that the proposed model achieves consistent improvements across multiple benchmarks.On the Det-Fly dataset,it improves precision by 3%,recall by 5.6%,and mAP50 by 4.5%compared with the baseline,while reducing parameters by 21.2%.Cross-validation on the VisDrone dataset further validates its robustness,yielding additional gains of 3.2%in precision,6.1%in recall,and 4.8%in mAP50 over the original YOLOv8.These findings confirm the effectiveness of the proposed algorithm in enhancing UAV detection performance under complex scenarios. 展开更多
关键词 Small target detection anti-drone yolov8 shared convolution feature fusion network
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Enhancing Intrusion Detection Systems Using Hybrid AI-Based Approaches 认领 引用 被引量：1
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作者 Mohammad Alshinwan Radwan M.Batyha +5 位作者 Walaa Alayed Saad Said Alqahtany Suhaila Abuowaida Hamza A.Mashagba Azlan B.Abd Aziz Samir Salem Al-Bawri 《Computers, Materials & Continua》 SCIE EI 2026年第5期2208-2245,共38页
Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics.Although numerousmachine-learning-based intrusion detection systems(IDS)have... Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics.Although numerousmachine-learning-based intrusion detection systems(IDS)have been developed,their effectiveness is often constrained by high dimensionality and redundant features that degrade both accuracy and efficiency.This study introduces a hybrid feature-selection framework that integrates the exploration capability of Prairie Dog Optimization(PDO)with the exploitation behavior of Ant Colony Optimization(ACO).The proposed PDO–ACO algorithm identifies a concise yet discriminative subset of features from the NSLKDD dataset and evaluates them using a Support Vector Machine(SVM)classifier.Experimental analyses reveal that the PDO–ACO model achieves superior detection accuracy of 98%while significantly lowering false alarms and computational overhead.Further validation on the CEC2017 benchmark suite confirms the robustness and adaptability of the hybrid model across diverse optimization landscapes,positioning PDO–ACO as an efficient and scalable approach for intelligent intrusion detection. 展开更多
关键词 Intrusion detection system prairie dog optimization artificial bee colony support vector machine
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Rapid on-site genotyping of the ovine prolific FecBBmutation using a CRISPR/Cas12a-based detection system 认领 引用 被引量：2
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作者 Tingjie Wu Jiayuan Sun +5 位作者 Lijin Lu Chen Wang Shiwei Zhou Yulin Chen Xinjie Wang Xiaolong Wang 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第4期1597-1605,共9页
BMPR1B is a pivotal gene that influences reproductive performance in sheep.The sheep populations that carry the FecBBmutation within this gene exhibit significantly higher lambing rates compared to wild-type popula... BMPR1B is a pivotal gene that influences reproductive performance in sheep.The sheep populations that carry the FecBBmutation within this gene exhibit significantly higher lambing rates compared to wild-type populations.Therefore,screening for individuals carrying the FecBBmutation is crucial for effective sheep breeding programs.This study aims to establish a rapid,precise,and visualised on-site detection method for genotyping the prolific FecBBmutation in sheep.We combined the CRISPR/Cas12a system with the recombinase-polymerase amplification(RPA)technique.We introduced an additional nucleotide mismatch on the amplification primers to form a Cas12a-recognised protospacer adjacent motif(PAM)sequence.In addition,mismatches were introduced in CRISPR-derived RNA(crRNA)to enable naked-eye differentiation of the assay results.Subsequently,we validated the accuracy of the method by examining additional blood samples from 56 sheep representing four breeds.The results of using our developed system were highly consistent with the Sanger sequencing.Overall,the CRISPR/Cas12a-based detection provides a rapid and more versatitle method for FecBBgenotyping.It holds promise in enhancing efficiency in livestock breeding programmes for any single nucleotide mutations. 展开更多
关键词 CRISPR/Cas12a isothermal amplification SNP detection BMPR1B FecBBmutation sheep
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Global-local feature optimization based RGB-IR fusion object detection on drone view 认领 引用 被引量：1
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作者 Zhaodong CHEN Hongbing JI Yongquan ZHANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第1期436-453,共18页
Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still st... Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at http://gffzz188fe103f8f1460as5pcwpcvv9x9w6qv9.ffgz.tsg.suse.edu.cn/lao chen330/GLFDet. 展开更多
关键词 Object detection Deep learning RGB-IR fusion Drones Global feature Local feature
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Near-infrared mechanoluminescence from Sr3Sn2O7:Nd3+ for potential bioimaging and non-destructive detection 认领 引用 被引量：2
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作者 Yafen Wu Sheng Wu +1 位作者 Puxian Xiong Shouping Wang 《Journal of Rare Earths》 SCIE EI CAS CSCD 2026年第1期85-93,共9页
Mechanoluminescence(ML) materials have made significant progress in flexible sensing and human-computer interaction due to their unique "force-to-light" conversion properties.However,most ML materials are li... Mechanoluminescence(ML) materials have made significant progress in flexible sensing and human-computer interaction due to their unique "force-to-light" conversion properties.However,most ML materials are limited to the visible light spectral band and single triggering mechanisms,limiting their practical applications in bioimaging and medical non-destructive detection.Here,a nearinfrared(NIR) ML material Sr3Sn2O7:0.01Nd3+ with multi-mode physical stimuli response is reported.Under friction and compression mechanical stimuli,the ML intensity of Sr3Sn2O7:0.01Nd3+ is linearly positively correlated with the applied load,showing excellent stress-sensing performance.At the same time,the Sr3Sn2O7:0.01Nd3+ ML material can penetrate different biological tissues up to 10 mm thick and realize the non-destructive detection of components in various transparent liquids.In addition,non-invasive blood lipid detection can be potentially obtained for patients through the absorption characteristics of oils and fats for NIR light signals.These research results indicate the broad application prospects of Sr3Sn2O7:0.01Nd3+ in bioimaging,health monitoring,and non-destructive detection,and it provides a basis for developing new medical non-invasive detection technologies. 展开更多
关键词 Sr3Sn2O7 Nd3+ Near-infrared mechanoluminescence Non-destructive detection Bioimaging Rare earths
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Intrusion Detection Systems in Industrial Control Systems:Landscape,Challenges and Opportunities 认领 引用 被引量：1
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作者 Tong Wu Dawei Zhou +1 位作者 Qingyu Ou Fang Luo 《Computers, Materials & Continua》 SCIE EI 2026年第3期162-200,共39页
The increasing interconnection of modern industrial control systems(ICSs)with the Internet has enhanced operational efficiency,but alsomade these systemsmore vulnerable to cyberattacks.This heightened exposure has dri... The increasing interconnection of modern industrial control systems(ICSs)with the Internet has enhanced operational efficiency,but alsomade these systemsmore vulnerable to cyberattacks.This heightened exposure has driven a growing need for robust ICS security measures.Among the key defences,intrusion detection technology is critical in identifying threats to ICS networks.This paper provides an overview of the distinctive characteristics of ICS network security,highlighting standard attack methods.It then examines various intrusion detection methods,including those based on misuse detection,anomaly detection,machine learning,and specialised requirements.This paper concludes by exploring future directions for developing intrusion detection systems to advance research and ensure the continued security and reliability of ICS operations. 展开更多
关键词 Industrial control system industrial control system network security intrusion detection cyberspace security ICS network network security
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CAGE-YOLO:A Dense Small Object Detection Model for Aquaculture Net Cages Based on Remote Sensing Images 认领 引用 被引量：1
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作者 ZHANG Wenbo JIANG Yijue +2 位作者 SONG Wei HE Qi ZHANG Wenbo 《智慧农业（中英文）》 CSCD 2026年第2期98-117,共20页
[Objective]Detecting dense and small aquaculture net cages in complex backgrounds is difficult,the purpose of this study is to build a specialized dataset and design a targeted detection model that enhances recognitio... [Objective]Detecting dense and small aquaculture net cages in complex backgrounds is difficult,the purpose of this study is to build a specialized dataset and design a targeted detection model that enhances recognition accuracy and robustness for practical aquaculture management.[Methods]A dataset of aquaculture net cages was constructed using highresolution remote sensing imagery collected from seven representative farming regions(Australia,Canada,Chile,Croatia,Greece,China,and the Faroe Islands),and Cage-YOLO,a deep learning model based on YOLOv5,was proposed for detecting dense and small aquaculture net cages.First,an adaptive dense perception algorithm was introduced,which automatically selects and generates feature maps that reflect the high-density distribution of small aquaculture net cages.Second,an enhanced module based on spatial pyramid pooling fast was integrated to effectively reduce background noise interference and improve global feature extraction capabilities.Finally,a mixed attention block was incorporated to further enhance the model's perception of dense and small objects.[Results and Discussions]Experimental results showed that the proposed Cage-YOLO achieved improvements over the original YOLOv5 in terms of precision,recall,and mean average precision by 5.6,21.8,and 17.4 percentage points,respectively.The model size was maintained at 16.9 MB,demonstrating both strong performance and deployment advantages.[Conclusions]This study provides a new approach for dense and small object detection and offers technical support for the intelligent management of marine cage aquaculture. 展开更多
关键词 aquaculture net cage small object detection adaptive dense perception algorithm enhancement spatial pyramid pooling mix attention block
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Rapid and ultrasensitive point-of-care detection of ASFV antibodies using p30-Fc-labeled nanoparticle-based fluorescence strip-assisted portable immunosensor 认领 引用
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作者 Yang Yang Jiayang Zheng +10 位作者 Yan Zhang Qianming Zhao Yafang Lin Junjie Zhang Zongjie Li Ke Liu Beibei Li Donghua Shao Yafeng Qiu Zhiyong Ma Jianchao Wei 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第6期2630-2633,共4页
Highlights A portable immunosensor device assisted by p30-Fc-labeled nanoparticle-based fiuorescence strips was developed for the rapid detection of African swine fever virus(ASFV)antibodies in the field.The fluoresce... Highlights A portable immunosensor device assisted by p30-Fc-labeled nanoparticle-based fiuorescence strips was developed for the rapid detection of African swine fever virus(ASFV)antibodies in the field.The fluorescent test strip prepared in this study has the advantages of low cost,high specificity and sensitivity,good repeatability and stability.The fiuorescent test strip detection method in this study has extremely high sensitivity and antibodies can be detected four days after ASFV infection,which has high potential application value in the monitoring and control of African swine fever. 展开更多
关键词 fiuorescence strips portable immunosensor device african swine fever ultrasensitive detection fluorescence strip rapid detection fiuorescent test strip detection p fc labeled nanoparticle
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Highly resistive lead-free perovskite ferroelectric enables self-driven X-ray detection with ultralow dose 认领 引用
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作者 Huawei Yang Jianbo Wu +11 位作者 Zeng-Kui Zhu Hang Li Panpan Yu Ying Zeng Qingran Ding Guirong Chen Lijun Xu Zhangtong Han Haiqing Zhong Yueying Wang Wenhui Wu Junhua Luo 《Chinese Chemical Letters》 SCIE CAS CSCD 2026年第3期746-751,共6页
Lead-free perovskite has become a shining pearl in the field of direct X-ray detection due to its nontoxicity and excellent optoelectronic properties.However,the high limit of detection(LoD)of X-ray detectors due to h... Lead-free perovskite has become a shining pearl in the field of direct X-ray detection due to its nontoxicity and excellent optoelectronic properties.However,the high limit of detection(LoD)of X-ray detectors due to high current noise caused by high operating voltages is a major challenge to overcome.Here,we utilized a zero-dimensional lead-free perovskite ferroelectric material(NMP)3Sb2Br9(1,NMP=Nmethylpyrrolidine)to achieve ultra-low Lo D self-driven X-ray detection.The self-driven detection mode without external bias has been proven to be an effective means of reducing Lo D due to its low current noise characteristics.Additionally,the zero-dimensional distinctive isolated framework results in a high resistivity of 1.39×1011Ωcm,which effectively reduces the current noise and suppresses ion migration.By further combining the ferroelectric-induced bulk photovoltaic effect,the 1-based detector achieves an ultra-low Lo D X-ray detection of 84.1 n Gyair/s under the self-driven mode,which represents a quite advanced level in the lead-free perovskite X-ray detection region.Our work successfully achieved ultra-low Lo D self-driven X-ray detection by combining ferroelectricity with high resistance,providing a promising avenue for the development of low Lo D X-ray detectors. 展开更多
关键词 Zero-dimensional perovskite Lead-free Ferroelectric Self-driven X-ray detection Low limit of detection
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Scalable and Resilient AI Framework for Malware Detection in Software-Defined Internet of Things 认领 引用 被引量：1
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作者 Maha Abdelhaq Ahmad Sami Al-Shamayleh +2 位作者 Adnan Akhunzada Nikola Ivkovi´c Toobah Hasan 《Computers, Materials & Continua》 SCIE EI 2026年第4期1307-1321,共15页
The rapid expansion of the Internet of Things(IoT)and Edge Artificial Intelligence(AI)has redefined automation and connectivity acrossmodern networks.However,the heterogeneity and limited resources of IoT devices expo... The rapid expansion of the Internet of Things(IoT)and Edge Artificial Intelligence(AI)has redefined automation and connectivity acrossmodern networks.However,the heterogeneity and limited resources of IoT devices expose them to increasingly sophisticated and persistentmalware attacks.These adaptive and stealthy threats can evade conventional detection,establish remote control,propagate across devices,exfiltrate sensitive data,and compromise network integrity.This study presents a Software-Defined Internet of Things(SD-IoT)control-plane-based,AI-driven framework that integrates Gated Recurrent Units(GRU)and Long Short-TermMemory(LSTM)networks for efficient detection of evolving multi-vector,malware-driven botnet attacks.The proposed CUDA-enabled hybrid deep learning(DL)framework performs centralized real-time detection without adding computational overhead to IoT nodes.A feature selection strategy combining variable clustering,attribute evaluation,one-R attribute evaluation,correlation analysis,and principal component analysis(PCA)enhances detection accuracy and reduces complexity.The framework is rigorously evaluated using the N_BaIoT dataset under k-fold cross-validation.Experimental results achieve 99.96%detection accuracy,a false positive rate(FPR)of 0.0035%,and a detection latency of 0.18 ms,confirming its high efficiency and scalability.The findings demonstrate the framework’s potential as a robust and intelligent security solution for next-generation IoT ecosystems. 展开更多
关键词 AI-driven malware analysis advanced persistent malware(APM) AI-poweredmalware detection deep learning(DL) malware-driven botnets software-defined internet of things(SD-IoT)
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CoPt graphitic nanozyme enabled naked-eye identification and colorimetric/fluorescent dual-mode detection of phenylenediamine isomers 认领 引用
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作者 Luyao Guan Zhaoxin Wang +2 位作者 Shengkai Li Phouphien Keoingthong Zhuo Chen 《Chinese Chemical Letters》 SCIE CAS CSCD 2026年第2期407-414,共8页
Simultaneous identification and quantitative detection of phenylenediamine(PDA)isomers,including o-phenylenediamine(OPD),m-phenylenediamine(MPD),and p-phenylenediamine(PPD),are essential for environmental risk assessm... Simultaneous identification and quantitative detection of phenylenediamine(PDA)isomers,including o-phenylenediamine(OPD),m-phenylenediamine(MPD),and p-phenylenediamine(PPD),are essential for environmental risk assessment and human health protection.However,current visual detection methods can only distinguish individual PDA isomers and failed to identify binary or ternary mixtures.Herein,a highly active and ultrastable peroxidase(POD)-like CoPt graphitic nanozyme was used for naked-eye identification and colorimetric/fluorescent(FL)dual-mode quantitative detection of PDA isomers.The CoPt@G nanozyme effectively catalyzed the oxidation of OPD,MPD,PPD,OPD+PPD,OPD+MPD,MPD+PPD and OPD+MPD+PPD into yellow,colorless,lilac,yellow,yellow,wine red and reddish-brown products,respectively,in the presence of H2O2.Thus,the MPD,PPD,MPD+PPD and OPD+MPD+PPD were easily identified based on the distinct color of their oxidation products,and the OPD,OPD+PPD,OPD+MPD could be further identified by the additional addition of MPD or PPD.Subsequently,CoPt@G/H2O2-,a 3,3′,5,5′-tetramethylbenzidine(TMB)/CoPt@G/H2O2-,and MPD/CoPt@G/H2O2-enabled colorimetric/FL dual-mode platforms for the quantitative detection of OPD,MPD and PPD were proposed.The experimental results illustrated that the constructed sensing platforms exhibit satisfactory sensitivity,comparable to that reported in previous studies.Finally,the evaluation of PDAs in water samples was realized,yielding satisfactory recoveries.This work expanded the application prospects of nanozymes in assessing environmental risks and protection of human security. 展开更多
关键词 Copt graphitic nanozyme Phenylenediamine isomers Naked-eye identification Colorimetric detection Fluorescent detection
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Pulse-shaped femtosecond-laser-modified Si:S photodetector for ultrawide-spectrum focal plane arrays and weak-light detection 认领 引用
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作者 Jiaxin Cao Ziyang Zheng +3 位作者 Xu Zhou Guanting Song Qiang Wu Jingjun Xu 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第3期840-853,共14页
High-performance Si photodetectors featuring an ultrawide spectral range can be realized through femtosecond(fs)-laser modification,enabling their broad application in focal plane arrays(FPAs)for artificial intelligen... High-performance Si photodetectors featuring an ultrawide spectral range can be realized through femtosecond(fs)-laser modification,enabling their broad application in focal plane arrays(FPAs)for artificial intelligence and complex operational scenarios.However,laser-induced surface inhomogeneity and elevated dark currents reduce the signal-to-noise ratio of FPAs in image detector arrays.To address these challenges,a pulse-shaping technique is employed here to precisely control light-matter interactions during laser modification.This approach not only improves the uniformity of surface microstructures but also significantly suppresses dark currents.The optimized device exhibits high responsivity across the visible to near-infrared spectrum,with a peak responsivity of 164.17 A·W-1 at−2.5 V.Notably,the photodetector demonstrates exceptional weak-light detection capacity at room temperature owing to its record-high specific detectivity of 1.71×1014 Jones,surpassing that of all previously reported Si-based photodetectors.These results highlight substantial advancements in the application of fs-laser-modified Si photodetectors,underscoring their potential in fields such as autonomous driving,weak-light detection,and medical monitoring. 展开更多
关键词 femtosecond laser modification pulse shaping visible-near-infrared Si photodetector weak-light detection infrared detection focal plane arrays
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An Efficient and Dynamic Framework for Multi-Scale Target Detection of Underwater Organisms 认领 引用
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作者 LI Zhuang LI Guixiang +1 位作者 SONG Xiangyang WANG Xinhua 《Journal of Ocean University of China》 SCIE CAS CSCD 2026年第1期150-160,共11页
The continuous decrease in global fishery resources has increased the importance of precise and efficient underwater fish monitoring technology.First,this study proposes an improved underwater target detection framewo... The continuous decrease in global fishery resources has increased the importance of precise and efficient underwater fish monitoring technology.First,this study proposes an improved underwater target detection framework based on YOLOv8,with the aim of enhancing detection accuracy and the ability to recognize multi-scale targets in blurry and complex underwater environments.A streamlined Vision Transformer(ViT)model is used as the feature extraction backbone,which retains global self-attention feature extraction and accelerates training efficiency.In addition,a detection head named Dynamic Head(DyHead)is introduced,which enhances the efficiency of processing various target sizes through multi-scale feature fusion and adaptive attention modules.Furthermore,a dynamic loss function adjustment method called SlideLoss is employed.This method utilizes sliding window technology to adaptively adjust parameters,which optimizes the detection of challenging targets.The experimental results on the RUOD dataset show that the proposed improved model not only significantly enhances the accuracy of target detection but also increases the efficiency of target detection. 展开更多
关键词 underwater target detection complex underwater environment YOLOv8 object detection
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