【摘要】 针对光伏板内部缺陷目标小且尺寸差异大的问题，提出了一种基于YOLOv8n的改进模型YOLO-RMFP。首先，将高效多尺度注意力机制与感受野注意力相结合，提出了一种感受野混合注意力机制，使模型聚焦不同尺度的特征，并解决高效多尺度注意力机制参数共享问题，提升光伏板微小缺陷检测精度。其次，将感受野混合注意力机制与空间金字塔池化模块结合，增强模型对多尺度特征的捕捉能力及对复杂特征区域的关注度，使模型在复杂背景下能够有效剔除噪声并增强鲁棒性，进一步增强光伏板缺陷小目标的检测精度。然后，将YOLOv8n主干网络中不同分辨率的特征映射与改进后的多尺度特征融合金字塔网络相结合，进一步增强了特征信息的交互性，以实现更全面的特征提取并增强目标检测的检测性能。最后，在PIoU的基础上，通过改变缺陷样本难易的权重，提升目标定位的精确度，有效缓解了光伏板缺陷样本不平衡问题。通过消融实验和对比实验的结果表明，YOLO-RMFP网络模型的检测精度mAP@0.5和mAP@0.5:0.95值分别提高3.1%和6.5%,精准度和召回率分别提升了4.2%和3.5%。满足了光伏板缺陷检测的评估要求。更多还原

【Abstract】 To address the challenge of small and highly variable defect sizes in photovoltaic panels, an improved YOLOv8n-based model named YOLO-RMFP is proposed. First, by integrating an efficient multi-scale attention mechanism with receptive field attention, a Receptive Field Mixed Attention mechanism is introduced. This mechanism enables the model to focus on features at multiple scales while addressing the parameter-sharing limitations of conventional multi-scale attention, thereby enhancing the detection accuracy for tiny defects in photovoltaic panels. Second, the Receptive Field Mixed Attention mechanism is integrated with the Spatial Pyramid Pooling module to enhance the model’s capability to capture multi-scale features and focus on complex regions. This integration improves the model’s ability to suppress noise in complex backgrounds, thereby further boosting the detection precision of small defects in photovoltaic panels. Then, feature maps of different resolutions from the YOLOv8n backbone are fused with an improved multi-scale feature fusion pyramid network. This enhances the interaction of feature information, enabling more comprehensive feature extraction and improving overall detection performance. Finally, based on the PIOU loss function, the model adjusts the weightings of defect samples according to their detection difficulty. This improves the localization accuracy and effectively mitigates the problem of sample imbalance in photovoltaic defect detection. Results from ablation and comparative experiments show that the YOLO-RMFP model improves detection accuracy, with mAP@0.5 and mAP@0.5:0.95 increasing by 3.1% and 6.5%, respectively. Precision and recall are also enhanced by 4.2% and 3.5%, respectively. These results demonstrate that the proposed model meets the performance requirements for photovoltaic panel defect detection.更多还原