Intelligent Recognition of Bird Species Related to Power Grid Faults Based on Object Detection

Power grid faults caused by bird activities reveal an upward trend. In order to assist transmission line inspector, recognize the bird species, in this paper, an object detection method based on YOLOv4 is presented for image recognition of birds related to power faults. An image dataset including the typical harmful bird species is constructed using the inspection images and network resources, and the image annotation and data augmentation are also realized. The YOLOv4 detection model is established and trained by the multi-stage transfer learning. Three methods are introduced to improve the training results, including the Mosaic data enhancement, the cosine annealing attenuation and the label smoothing. The optimal detection model is obtained by analyzing the influences of different factors like anchor number, training method, and sample size, etc. on the test results. The image test set including 20 bird species and 1134 true objects is detected, and the mean average precision (E_mAP) reaches 92.2%. The detection results of the YOLOv4 are compared with those of the Faster RCNN, the SSD and the YOLOv3, which shows that the YOLOv4 model has a higher precision and less false detection number. This study indicates that the YOLOv4 model is able to detect the bird objects in transmission line inspection images and achieve bird recognition, which will provide reference for differential prevention of the bird-related outages.