Recognition of Bird Nests on Power Transmission Lines in Aerial Images Based on Improved YOLOv4 - Zhang, Zhaoyun; He, Guanfeng (Frontiers in Energy Research, 2022)
Bird nests on transmission line towers pose a serious threat to the safe operation of power systems. Exploring an effective method to detect bird nests taken by drone inspection is crucial. However, the images taken by drones have problems such as drastic changes in the size of the object, occlusion of the object, and inconsistency in the characteristics of the object in relation to the background. The original YOLOv4 model has difficulty solving these problems. Therefore, this article improves the original YOLOv4 model by adding a Swin transformer block to its backbone network, fusing the attention mechanism into the neck of the original model, implementing classification and regression tasks for head decoupling, and using an anchor-free frame strategy and the SimOTA sample allocation method. The improved model was trained and tested on a bird nest dataset, and the detection accuracy reached 88%. Finally, the method was compared and evaluated against Faster R-CNN, RetinaNet, SSD, and the original YOLOv4, four of the other mainstream object detection models. The results showed that the accuracy obtained by the algorithm was better than the other models; the algorithm could effectively detect difficult objects such as multiple angles, occlusions, and small objects, and the detection speed could meet the real-time requirements.