Tank Armored Vehicle Target Detection Based on Improved YOLOv5s

For striking enemy armored vehicles from the air or on the ground, it is a necessary to timely mastery enemy tanks and armored vehicles by UAVs on the battlefield. The traditional target detection algorithms have shortcomings such as insufficient accuracy and slow computing speed, which make the monitoring of target armored vehicles still have many problems. In this paper, through a large amount of image data containing armored vehicles, the improved YOLOv5s algorithm is used in the field of deep learning for target detection of armored vehicles. Firstly, the SimAM attention mechanism is combined with the C3 module in the Backbone network to improve the network's ability to process image fine textures. Subsequently, the up-sampling method of the original network is replaced with the CARAFE module. The experimental results show that the detection precision and recall of the improved network model are increased by 1.823% and 6.89%, respectively, which effectively improves the accuracy of UAVs in recognizing tank-armored vehicles.