Improved YOLOv4 for real-time detection algorithm of low-slow-small unmanned aerial vehicles

In order to solve the low accuracy in low-slow-small unmanned aerial vehicles (UAVs) mission on embedded platform and deployment problem of poor real-time performance, a small UAV target detection algorithm based on improved YOLOv4 was proposed. By increasing the shallow characteristic figure, improving the anchor, enhancing the small target, and the detection performance of network for small target was improved, through sparse training and model pruning, the model running time was greatly reduced. The average accuracy (mAP) reaches 85.8% on the 1080Ti, and the frame rate (FPS) reaches 75 frame/s, which achieving network lightweight. This lightweight model was deployed on the Xavier edge computing platform, which could achieve the UAV target detection speed of 60 frame/s. Experimental results show that, in compared with YOLOv4 and YOLOv4-TINY, this algorithm achieves the balance of running speed and detection accuracy, and can effectively solve the problem of UAV target detection on embedded platform.