基于改进YOLOv4算法的高压塔鸟巢检测

针对现有算法对高压塔上鸟巢检测存在参数量过大，实时性不足及对小目标检测能力较弱的问题，提出了一种改进的YOLOv4算法。首先使用Mobilenetv2网络代替CSPDarknet53网络作为主干网络，减少算法的参数量且提升检测速度；同时在Mobilenetv2网络的逆残差网络中嵌入注意力Coordinate Attention模块，增强网络对目标特征提取能力。然后，对PANet网络进行改进，获取更多的细节特征信息，提高对小目标鸟巢的检测能力。最后，使用Focal Loss函数优化损失函数，降低大量简单背景样本训练的权重，提升对小目标鸟巢困难样本训练的侧重，进一步提高对小目标鸟巢的检测能力。实验结果表明，较原始的YOLOv4算法，改进后的YOLOv4算法的参数量减少了48.1%，检测速度和精度分别提高了12.9fps和2.33%。即改进后的YOLOv4算法大幅度减少了算法参数量，且对鸟巢的检测拥有更好的检测性能。

Bird's nest detection of high voltage tower based on improved YOLOv4 algorithm

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.