KBN-YOLOv5: Improved YOLOv5 for Detecting Bird’s Nest in High-Voltage Tower

The problem of birds nesting in high-voltage towers has laid a major hidden danger to the safe operation of long-distance transmission lines. In the power line inspection, the image background of the bird’s nest is complex. Some nests are small and locally occluded, and it is difficult for existing object detection algorithms to detect with low computational effort under high accuracy. To solve the above problems, we recommend using KBN-YOLOv5. Based on YOLOv5, we use K-means algorithm to cluster the size of bird nest image to set the size of anchor frame. In the Backbone network, we replace the Focus and SPP modules with Conv and SPPF modules, respectively, and adjust the number of BottleneckCSP modules as well as their positions. Finally, in the Neck network, we improve the BottleneckCSP module combined with the Efficient Channel Attention (ECA) module for a more effective weight information distribution to accomplish a more detailed detection capability. The experimental results show that KBN-YOLOv5 demonstrates better performance with other mainstream algorithms in terms of combined performance of detection accuracy and model computation. Compared to baseline model (YOLOv5), the Recall and mAP values of KBN-YOLOv5 reach 92.3% and 96.0%, which improve 6.7% and 5.4%, respectively. In addition, KBN-YOLOv5 has good robustness, and the model computation is reduced by 6.7% while detection accuracy of the model is improved.