In order to address the challenges posed by complex background interference,high miss-detection rates of micro-scale defects,and limited model deployment efficiency in photovoltaic(PV)module defect detection,this pape...In order to address the challenges posed by complex background interference,high miss-detection rates of micro-scale defects,and limited model deployment efficiency in photovoltaic(PV)module defect detection,this paper proposes an efficient detection framework based on an improved YOLOv11 architecture.First,a Re-parameterized Convolution(RepConv)module is integrated into the backbone to enhance the model’s sensitivity to fine-grained defects—such as micro-cracks and hot spots—while maintaining high inference efficiency.Second,a Multi-Scale Feature Fusion Convolutional Block Attention Mechanism(MSFF-CBAM)is designed to guide the network toward critical defect regions by jointly modeling channel-wise and spatial attention.This mechanism effectively strengthens the specificity and robustness of feature representations.Third,a lightweight Dynamic Sampling Module(DySample)is employed to replace conventional upsampling operations,thereby improving the localization accuracy of small-scale defect targets.Experimental evaluations conducted on the PVEL-AD dataset demonstrate that the proposed RMDYOLOv11 model surpasses the baseline YOLOv11 in terms of mean Average Precision(mAP)@0.5,Precision,and Recall,achieving respective improvements of 4.70%,1.51%,and 5.50%.The model also exhibits notable advantages in inference speed and model compactness.Further validation on the ELPV dataset confirms the model’s generalization capability,showing respective performance gains of 1.99%,2.28%,and 1.45%across the same metrics.Overall,the enhanced model significantly improves the accuracy of micro-defect identification on PV module surfaces,effectively reducing both false negatives and false positives.This advancement provides a robust and reliable technical foundation for automated PV module defect detection.展开更多
A forest fire is a natural disaster characterized by rapid spread,difficulty in extinguishing,and widespread destruction,which requires an efficient response.Existing detection methods fail to balance global and local...A forest fire is a natural disaster characterized by rapid spread,difficulty in extinguishing,and widespread destruction,which requires an efficient response.Existing detection methods fail to balance global and local fire features,resulting in the false detection of small or hidden fires.In this paper,we propose a novel detection technique based on an improved YOLO v5 model to enhance the visual representation of forest fires and retain more information about global interactions.We add a plug-and-play global attention mechanism to improve the efficiency of neck and backbone feature extraction of the YOLO v5 model.Then,a re-parameterized convolutional module is designed,and a decoupled detection head is used to accelerate the convergence speed.Finally,a weighted bi-directional feature pyramid network(BiFPN)is introduced to merge feature information for local information processing.In the evaluation,we use the complete intersection over union(CIoU)loss function to optimize the multi-task loss for different kinds of forest fires.Experiments show that the precision,recall,and mean average precision are increased by 4.2%,3.8%,and 4.6%,respectively,compared with the classic YOLO v5 model.In particular,the mAP@0.5:0.95 is 2.2% higher than the other detection methods,while meeting the requirements of real-time detection.展开更多
基金supported by the Gansu Provincial Department of Education Industry Support Plan Project(2025CYZC-018).
文摘In order to address the challenges posed by complex background interference,high miss-detection rates of micro-scale defects,and limited model deployment efficiency in photovoltaic(PV)module defect detection,this paper proposes an efficient detection framework based on an improved YOLOv11 architecture.First,a Re-parameterized Convolution(RepConv)module is integrated into the backbone to enhance the model’s sensitivity to fine-grained defects—such as micro-cracks and hot spots—while maintaining high inference efficiency.Second,a Multi-Scale Feature Fusion Convolutional Block Attention Mechanism(MSFF-CBAM)is designed to guide the network toward critical defect regions by jointly modeling channel-wise and spatial attention.This mechanism effectively strengthens the specificity and robustness of feature representations.Third,a lightweight Dynamic Sampling Module(DySample)is employed to replace conventional upsampling operations,thereby improving the localization accuracy of small-scale defect targets.Experimental evaluations conducted on the PVEL-AD dataset demonstrate that the proposed RMDYOLOv11 model surpasses the baseline YOLOv11 in terms of mean Average Precision(mAP)@0.5,Precision,and Recall,achieving respective improvements of 4.70%,1.51%,and 5.50%.The model also exhibits notable advantages in inference speed and model compactness.Further validation on the ELPV dataset confirms the model’s generalization capability,showing respective performance gains of 1.99%,2.28%,and 1.45%across the same metrics.Overall,the enhanced model significantly improves the accuracy of micro-defect identification on PV module surfaces,effectively reducing both false negatives and false positives.This advancement provides a robust and reliable technical foundation for automated PV module defect detection.
基金supported by the Graduate Research and Innovation Projects of Jiangsu Province(No.SJCX23_0320).
文摘A forest fire is a natural disaster characterized by rapid spread,difficulty in extinguishing,and widespread destruction,which requires an efficient response.Existing detection methods fail to balance global and local fire features,resulting in the false detection of small or hidden fires.In this paper,we propose a novel detection technique based on an improved YOLO v5 model to enhance the visual representation of forest fires and retain more information about global interactions.We add a plug-and-play global attention mechanism to improve the efficiency of neck and backbone feature extraction of the YOLO v5 model.Then,a re-parameterized convolutional module is designed,and a decoupled detection head is used to accelerate the convergence speed.Finally,a weighted bi-directional feature pyramid network(BiFPN)is introduced to merge feature information for local information processing.In the evaluation,we use the complete intersection over union(CIoU)loss function to optimize the multi-task loss for different kinds of forest fires.Experiments show that the precision,recall,and mean average precision are increased by 4.2%,3.8%,and 4.6%,respectively,compared with the classic YOLO v5 model.In particular,the mAP@0.5:0.95 is 2.2% higher than the other detection methods,while meeting the requirements of real-time detection.
