UAVs can provide target-related image information in such fields as military intelligence and aerial photography detection,providing target information for image processing tasks.An improved UAV image recognition algorithm based on YOLOv5s is proposed to address the issues of complex background,small detection targets and limited extractable features in UAV images.Firstly,the network structure is optimized by using CotNet module to enhance the model's self-learning ability and improve recognition accuracy.Secondly,the Neck network is improved to better utilize the rich information contained in shallow feature maps to locate targets through cross-layer linking and an improvement to feature map resolution.In the detection head section,decoupled detection heads are used to reduce the conflict between localization and classification tasks over feature information utilization in the prediction process.Finally,in order to improve the convergence rate and model accuracy,the aspect ratio of the width to the height of the loss function is optimized based on CIoU and EIoU loss functions.Tests are conducted on the test set of public dataset VisDrone.The proposed algorithm improves mAP50 and mAP50︰95 by 6.1 and 2.9 percentage points respectively in comparison with the original algorithm.The experimental results show that the proposed model can effectively improve the accuracy of UAV image recognition.