Aiming at the problems of excessive parameters, insufficient real-time performance and weak detection ability of small targets in the existing algorithms for bird's nest detection on high-voltage tower, an improved YOLOv4 algorithm is proposed. Firstly, Mobilenetv2 network is used to replace CSPDarknet53 network as the backbone network, which reduces the amount of parameters of the algorithm and improves the detection speed. At the same time, the Coordinate Attention module is embedded in the inverse residual network of Mobilenetv2 network, which enhance the ability of the network to extract target features. Then, the PANet network is improved to obtain more detailed feature information and improve the detection ability of small target bird's nest. Finally, the Focal Loss function is used to optimize the loss function, reduce the weight of a large number of simple background samples, and improve the focus on the difficult sample training of small target bird's nest, which further improves the detection ability of small target bird's nest. The experimental results show that compared with the original YOLOv4 algorithm, the parameters of the improved YOLOv4 algorithm are reduced by 48.1%, and the detection speed and accuracy are improved by 12.9fps and 2.33% respectively. That is, the improved YOLOv4 algorithm greatly reduces the amount of algorithm parameters, and has better detection performance for bird's nest detection.