Abstract: Transmission line safety is the premise of safe and stable operation of the power grid, but frequent bird activities have seriously affected the transmission line. In order to solve the drawbacks of the traditional bird-repelling method, the researchers use deep learning algorithms for bird detection. However, deep learning algorithm needs to run on a server with good performance, which will inevitably cause network delay and cannot be used to drive birds in real time. Therefore, bird detection should be carried out at the mobile terminal, but the existing target detection algorithm model is large and cannot be directly applied to the mobile terminal. Therefore, this paper proposes a bird detection algorithm for transmission line suitable for mobile terminal, which will be in the YOLO v3 model. This algorithm replaces the basic network darknet-53 in YOLO V3 model with the lightweight feature extraction network MobileNet, which achieves bird detection of transmission lines at mobile terminal. The experimental results show that the accuracy of the model can reach 83.57% and the detection speed reaches 61 fps in the bird detection task of the transmission line. It can be stably operated on the mobile terminal platform of 4 GB memory, which can meet the accuracy and real-time requirements of the bird detection task of transmission line and have a good application prospect.

Key words: transmission line, mobile terminal, bird detection, YOLO v3, deep learning