光伏电池的高效发电在推动绿色低碳循环发展中发挥着重要作用,针对光伏电池缺陷图像中存在背景复杂与目标尺寸较小等问题,提出一种改进YOLO11n的缺陷检测模型———CCMW-YOLO11n。首先,在YOLO11n的骨干网络中提出跨阶段部分改进模块(cro...光伏电池的高效发电在推动绿色低碳循环发展中发挥着重要作用,针对光伏电池缺陷图像中存在背景复杂与目标尺寸较小等问题,提出一种改进YOLO11n的缺陷检测模型———CCMW-YOLO11n。首先,在YOLO11n的骨干网络中提出跨阶段部分改进模块(cross stage partial improvement,CSP-I),该模块通过设计多头自注意力机制(multi-head self attention,MHSA)、卷积门控线性单元(convolutional gated linear uint,CGLU)与传统卷积(convolution,Conv)相结合,兼顾全局信息感知与局部特征提取,增强了多尺度特征的提取效果;其次,在特征融合阶段采用内容感知特征重组上采用技术(content-aware reassembly of features,CARAFE),该方法实现了对特征图自适应重组和细节增强,有效保留了细节特征,提升了模型对复杂目标的感知能力;然后,在颈部网络中融入混合聚合网络改进模块(mixed aggregation net enhancement,MAN-E),进一步增强了特征表达能力;最后,针对基础模型中CIoU损失函数的不足,结合WIoUv3、Inner-IoU和SIoU,提出一种新的边界框回归损失函数Wise-Inner-SIoU,以优化模型的回归效果。实验结果表明,改进后的CCMW-YOLO11n模型召回率提升了9.6%,mAP@0.5和mAP@0.5:0.95分别达到91.0%和61.1%,较基础模型分别提高了3.1%和2.0%,实现了对光伏电池缺陷的高效检测。展开更多
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
文摘受货车侧架去除区域复杂结构影响,针对在铸造清理视觉拍摄过程中二维图像存在颜色特征模糊等问题,提出一种基于改进YOLOv10n的侧架铸造残余检测算法。引入轻量级通用上采样算子——内容感知特征重组上采样算子(content aware reassembly of features,CARAFE)模块,有效增加了网络模型的整体感知范围以及对有效语义信息的利用,在不影响网络复杂度的同时提升了检测模型对残余特征的识别能力。提出一种轻量级Mobile_CA模块替换原主干网络,显著降低网络的复杂度。实验结果表明:改进后的网络在铸造残余检测任务中平均检测精度mAP@0.5为93.8%,平均检测速度为65.6帧/s,相较于原网络,mAP@0.5提高了1.1%,检测速度显著提升,能够满足工况对于检测精度和效率的要求。
文摘光伏电池的高效发电在推动绿色低碳循环发展中发挥着重要作用,针对光伏电池缺陷图像中存在背景复杂与目标尺寸较小等问题,提出一种改进YOLO11n的缺陷检测模型———CCMW-YOLO11n。首先,在YOLO11n的骨干网络中提出跨阶段部分改进模块(cross stage partial improvement,CSP-I),该模块通过设计多头自注意力机制(multi-head self attention,MHSA)、卷积门控线性单元(convolutional gated linear uint,CGLU)与传统卷积(convolution,Conv)相结合,兼顾全局信息感知与局部特征提取,增强了多尺度特征的提取效果;其次,在特征融合阶段采用内容感知特征重组上采用技术(content-aware reassembly of features,CARAFE),该方法实现了对特征图自适应重组和细节增强,有效保留了细节特征,提升了模型对复杂目标的感知能力;然后,在颈部网络中融入混合聚合网络改进模块(mixed aggregation net enhancement,MAN-E),进一步增强了特征表达能力;最后,针对基础模型中CIoU损失函数的不足,结合WIoUv3、Inner-IoU和SIoU,提出一种新的边界框回归损失函数Wise-Inner-SIoU,以优化模型的回归效果。实验结果表明,改进后的CCMW-YOLO11n模型召回率提升了9.6%,mAP@0.5和mAP@0.5:0.95分别达到91.0%和61.1%,较基础模型分别提高了3.1%和2.0%,实现了对光伏电池缺陷的高效检测。
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.
