YOLO-Q: Drone Aerial Target Detection

Abstract

Currently, with the widespread use of UAVs in various fields, there is a significant challenge in detecting aerial targets using drones. The YOLO series of object detection has made significant advancements in both speed and accuracy. However, many state-of-the-art methods are not suitable for drone images due to the unique perspective and large number of small targets. Increasing the detection layer or input picture size can improve accuracy, but this also increases computational cost and reduces detection speed. This paper proposes YOLOQ for aerial object detection, utilizing new S-FPN, SPPF+, and S-SIoU modules. Extensive experiments demonstrate that YOLOQ achieves advanced performance on generic drone dataset with only 5.1MB weight files, two million parameters, 13.5GFLOPs of computational complexity, and 192FPS speed.