Transmission Tower Condition Detection Algorithm Based on Improved YOLOv7
摘要
Transmission towers support power transmission lines and are critical to the operation of power systems. However, no dataset is currently available for detecting transmission tower status. To detect and warn about dangerous towers, this study first constructs an image dataset of tower status. The dataset comprises seven categories including foreign object intrusion, animal nests, base obstruction, and external damage. Next, the study proposes a lightweight algorithm, CT-YOLO, for detecting transmission tower status. This algorithm includes: 1)a lightweight backbone network, L-ELANnet, which reduces the parameter count by 3/4 while ensuring no significant change in detection accuracy; 2)a spatial pyramid pooling module based on the Efficient Channel Attention (ECA) mechanism, which achieves feature fusion at different scales with fewer parameters; 3)<i>k</i>-means++ to optimize the model's prior boxes, which improves the model's ability to learn about slender targets, such as debris and cranes, in the dataset; 4)Wise-IoU, a bounding box loss function, which provides dynamic non-monotonic gradient gains for data of different qualities, thereby improving training accuracy and convergence speed. Ablation and comparative experiments are conducted to verify the effectiveness and superiority of the improved model. Experimental results show that, compared to the original model, the proposed lightweight YOLO <i>k</i>-means++ algorithm increases mAP@0.5 from 94.9% to 95.4%, with a 21.5% increase in detection speed, reaching Frames Per Second (FPS) 113.6 frame/s. Furthermore, the model size is only 14.9 MB, which is 1/5 of the original model's. Overall, the improved model has higher detection accuracy and faster detection speed. Moreover, the proposed algorithm outperforms mainstream target detection algorithms in transmission tower condition detection.