Lightweight UAV Detection Algorithm Based on Improved YOLOv5

Aiming at the problem that the existing UAV detection algorithms cannot simultaneously take into account detection speed and accuracy，a lightweight UAV detection algorithm ，i. e.，Tiny Drone Real-time Detection-YOLO（TDRD-YOLO）based on YOLOv5s，is proposed in this paper. Firstly，the multi-scale fusion layer and output detection layer of YOLOv5s are used as the neck network and head network，respectively. MobileNetv3 lightweight network is introduced to reconstruct the original backbone network，and the channel behind the backbone network is compressed on the basis of the original YOLOv5s to reduce the size of the network model. Secondly，the attention mechanism of the Bneck module in the backbone network is modified from SE to CBAM （Convolutional Block Attention Module），and the CBAM is introduced in the neck network to make the network model pay more attention to the target features. Finally，the activation function of the neck network is modified as h-swish to further improve the accuracy of the model. Experimental results show that the average detection accuracy of the TDRD-YOLO algorithm proposed reaches 96.8%. Compared with YOLOv5s，the number of parameters is reduced by 11 times，the detection speed increases by 1.5 times，and the model size is reduced by 8.5 times. Experiments show that the proposed algorithm can greatly reduce the model size and improve the detection speed while maintaining good detection performance. © 2023 Hunan University. All rights reserved.