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
The precise and timely extraction of railway signals is crucial for the creation of railway electronic maps.This paper introduces a novel real-time detection approach for dynamically adjusting railway signals,leveragi...The precise and timely extraction of railway signals is crucial for the creation of railway electronic maps.This paper introduces a novel real-time detection approach for dynamically adjusting railway signals,leveraging an enhanced Real-Time DEtection TRansformer(RT-DETR)model.The enhancement involves the integration of a vision Transformer with Dynamically Quantifiable Sampling Attention Mechanism(DQSAM)into the ResNet50 backbone of the RT-DETR framework,thereby enhancing the model’s efficiency and accuracy in handling intricate visual tasks.Secondly,an ultra-lightweight and effective Dynamic Grouping upSampler(DyGSample)is inserted into the efficient hybrid encode module as the up-sampling part.This operator can effectively upsample the feature graph without increasing the computational burden,and improve the model resolution and detail capture ability.In addition,in order to solve the problem of deep layer of model network and high operating cost,a new bounding box similarity loss function of rotation intersection over union based on minimum point distance is adopted in this paper,which takes into account all relevant factors of existing loss functions,namely overlapping or non-overlapping regions,center point distance,width and height deviation,and simplifies the calculation process.As a lightweight signal detection model with ultra-fast,high real-time,and high precision,the detection accuracy of this method is improved from 90.21%to 97.45%,which proves the superior performance and effectiveness of the improved real-time dynamic adjustment RT-DETR model in railway signal extraction.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 Hunan Provincial Natural Science Foundation of China(Nos.2025JJ70018 and 2025JJ70057)the Hunan Provincial Key Research and Development Program(No.2024JK2065).
文摘The precise and timely extraction of railway signals is crucial for the creation of railway electronic maps.This paper introduces a novel real-time detection approach for dynamically adjusting railway signals,leveraging an enhanced Real-Time DEtection TRansformer(RT-DETR)model.The enhancement involves the integration of a vision Transformer with Dynamically Quantifiable Sampling Attention Mechanism(DQSAM)into the ResNet50 backbone of the RT-DETR framework,thereby enhancing the model’s efficiency and accuracy in handling intricate visual tasks.Secondly,an ultra-lightweight and effective Dynamic Grouping upSampler(DyGSample)is inserted into the efficient hybrid encode module as the up-sampling part.This operator can effectively upsample the feature graph without increasing the computational burden,and improve the model resolution and detail capture ability.In addition,in order to solve the problem of deep layer of model network and high operating cost,a new bounding box similarity loss function of rotation intersection over union based on minimum point distance is adopted in this paper,which takes into account all relevant factors of existing loss functions,namely overlapping or non-overlapping regions,center point distance,width and height deviation,and simplifies the calculation process.As a lightweight signal detection model with ultra-fast,high real-time,and high precision,the detection accuracy of this method is improved from 90.21%to 97.45%,which proves the superior performance and effectiveness of the improved real-time dynamic adjustment RT-DETR model in railway signal extraction.
