Abstract:In order to solve the adverse effect of the bird''s nest on the transmission line caused by the equipment and tower during the operation of the overhead transmission line, this paper compares and analyzes the advantages and disadvantages of the first-order target detection model and the second-order target detection model, and selects the RetinaNet model with the classification loss function as the core and the characteristic pyramid network as the backbone network for the automatic detection of the bird''s nest target. It solves the problem that the classical first-order target detection model and the second-order target detection model have low detection efficiency or accuracy. In this experiment, firstly, through data set selection and data set preprocessing, and through model training, the network structure and parameters are optimized and adjusted step by step, and a RetaiNet model suitable for nest detection is established to achieve rapid and accurate nest detection. The experimental results show that the average accuracy of RetinaNet model to the bird''s nest of transmission line is 94.1%, and the recognition speed of each picture is 68ms. By comparing with Faster R-CNN, YOlO and SSD methods, the validity and reliability of RetinaNet model to the bird''s nest detection on transmission line equipment and tower are verified.