基于RFCARep-YOLOv8n的光伏电池缺陷检测算法

doi:10.3778/j.issn.1002-8331.2405-0249

摘要/Abstract

摘要： 针对光伏电池缺陷图像存在目标遮掩、复杂背景以及人眼难以分辨的小目标缺陷等问题，提出一种基于感受野坐标注意力和重参数的YOLOv8n光伏电池缺陷检测算法，简记为RFCARep-YOLOv8n。提出一种基于感受野坐标注意力的重参数模块代替瓶颈模块进行特征提取，扩大对全局信息的关注度提高语义表达能力，抑制遮掩物和复杂背景的干扰；在快速空间金字塔池化后添加可分离大核聚集模块，通过提高长距离特征依赖增强全局特征信息融合；在特征融合部分使用多尺度序列特征融合颈部网络，结合多尺度辅助检测头，减少细节特征丢失，提高小目标缺陷检测能力。实验结果表明，该模型在PASCAL VOC数据集中较基准模型mAP@0.5和mAP@0.5：0.95分别提升2.3和2.1个百分点，同时在光伏缺陷数据集中mAP@0.5达到87.6%，较基准模型提升3.5个百分点，参数量为3.23×106，保持了基准模型的轻量参数同时提高检测性能。

关键词: 光伏缺陷, YOLOv8n, 感受野注意力, 特征融合, 重参数

Abstract: To address the problems of target occlusion, complex background and small target defects that are difficult for the human eye to distinguish in the defect images of photovoltaic cells, an photovoltaic cell defect detection algorithm with receptive field coordinated attention re-parameterization-YOLOv8n（RFCARep-YOLOv8n） is proposed. Firstly, a receptive-field coordinated attention re-parameterization block is proposed to replace the bottleneck block for feature extraction, expand the attention to global information to improve semantic expression ability, and suppress the interference of occlusion and complex background. Secondly, a large separable kernel attention module is added after spatial pyramid pooling-fast to enhance the global feature information fusion by improving the long-distance feature dependence. Finally, in the feature fusion part, multi-scale sequence fusion neck is used, and the multi-scale auxiliary detection head is combined to reduce the loss of detailed features and improve the detection ability of small target defects. Experimental results show that on the PASCAL VOC dataset the proposed model is 2.3 and 2.1 percentage points higher than the baseline model compared with mAP@0.5 and mAP@0.5:0.95, and on the photovoltaic defect dataset reaches 87.6% in mAP@0.5, which is 3.5 percentage points higher than the baseline model, and the parameter number is 3.23×106. The lightweight parameters of the benchmark model are maintained while the detection performance is improved.

Key words: photovoltaic defects, YOLOv8, receptive-field attention, feature fusion, re-parameterization

张冀, 王文彬, 余洋. 基于RFCARep-YOLOv8n的光伏电池缺陷检测算法[J]. 计算机工程与应用, 2025, 61(3): 131-143.

ZHANG Ji, WANG Wenbin, YU Yang. Defect Detection of Photovoltaic Cells Based on RFCARep-YOLOv8n[J]. Computer Engineering and Applications, 2025, 61(3): 131-143.

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