DF-YOLO: Highly Accurate Transmission Line Foreign Object Detection Algorithm

Ensuring the promptly removal of foreign objects from transmission lines is crucial for electricity safety. However, the existing object detection algorithms exhibit low precision and recall due to factors such as uncertainty in the type of foreign objects, imbalance of positive and negative samples, and the complexity of aerial photography backgrounds. Therefore, in this paper, an algorithm called DF-YOLO (Deformable Faster-You Only Look Once) for transmission line foreign object detection is proposed to resolve these problems. The algorithm is based on YOLOv7-Tiny and tailored to the dataset’s characteristics to achieve high precision along with excellent recall. The Focal-DIoU loss function is utilized to balance positive and negative sample proportions during training. Additionally, the algorithm incorporates the deformable convolution (DCN) module and the SimAM attention mechanism to enhance model performance, particularly in terms of foreign object recall and detection accuracy. Moreover, we optimized the network inference speed with the improved SPPCSPC_S-F module. Experimental results demonstrate that the improved DF-YOLO network achieves a 2.04% increase in mAP@.5 compared to the original YOLOv7-Tiny network. The recall rate also improves from 89% to 91.51%. Additionally, the inference speed of the network rises from 130 to 140 FPS, which enhances detection effectiveness and reduces the frequency of transmission line leaks triggered by foreign object incursion.