BT-YOLO: Improved YOLOv5 Based on BiFormer Structure and Task-Specific Decoupled Head for Photovoltaic Infrared Defect Detection on UAV Scenarios

Previous studies have demonstrated the importance of combining infrared defect detection methods with UAV inspection to promote the development of solar energy. The defect detection frequently encounters difficulties such as small objects, easy confusion between defects and environment, and uneven sample number, resulting in a low detection accuracy. To solve these problems, this paper proposed a model BT-YOLO to detect infrared photovoltaic images captured by UAV based on the YOLOv5 network. Firstly, BiFormer is a visual transformer structure embedded into the backbone network of the model, better preserving fine-grained details. Secondly, to achieve more accurate classification and finer localization, the feature encoding of classification and localization is decoded separately in the detection head. Finally, the regression loss is calculated using Wise-IoU instead of GIoU, thereby allowing the model to note the loss of ordinary-quality anchor boxes. The results demonstrate that the improved model improves the mAP performance by 5.1%.