Near-infrared defect detection method for photovoltaic panels based on the FBCT-YOLOv8 algorithm

In near-infrared defect detection of photovoltaic panels, challenges such as significant variations in defect scales, diversity and ambiguity in defect morphology, and difficulty in identifying small defect targets are often encountered. To address these issues, this paper proposes a photovoltaic panel near-infrared defect detection method based on the FBCT-YOLOv8 algorithm.Built upon YOLOv8, the proposed method first introduces omni-dimensional dynamic convolution (ODConv), which adaptively adjusts convolutional characteristics across all dimensions, enabling more precise alignment with input feature distributions and enhancing the extraction of critical information. Subsequently, a lightweight cross-scale feature fusion module (CCFM) is employed to efficiently integrate features at different scales, compensating for the limitations of single-scale features, significantly improving the network's adaptability to scale variations, and enhancing the detection accuracy of small defects.Furthermore, the dynamic head (DyHead) is adopted to unify attention-based detection heads, thereby improving detection performance. Finally, the InnerWIoU loss function is utilized to adapt to scenarios requiring dynamic adjustment of the loss focus.Experimental results demonstrate that the proposed FBCT-YOLOv8 algorithm reduces GFLOPs to 7.0 while achieving an mAP@50 of 0.91, meeting the real-time performance requirements for near-infrared defect detection of photovoltaic panels. In addition, ablation and comparative experiments further validate the effectiveness and superiority of the proposed method.