A More Efficient Algorithm for Small Target Detection in Unmanned Aerial Vehicles

Abstract

Due to the relatively high shooting altitude of unmanned aerial vehicles (UAV), the captured images often contain a multitude of small-scale targets. To solve the problems of small target scale, lack of semantic information, and high miss detection in drone target detection, in this paper we proposed a more effective unmanned aerial vehicle small target detection algorithm(MEU-YOLOv5) based on YOLOv5s. Firstly, an efficient global contextual module is proposed to enhance the algorithm's performance in feature extraction while reducing the excessive loss of shallow features. Secondly, a small-scale target detector is added to enhance the algorithm's detection capability for smaller targets. Lastly, a recursive multi-level feature fusion path is introduced to better fuse the shallow and deep features of the images, reducing overfitting and improving the algorithm's generalizability and robustness. Experimental results demonstrated that compared to YOLOv5s, MEU-YOLOv5 achieves a 7.4% improvement in [email protected] and a 4.9% improvement in [email protected]:0.95. Additionally, the overall performance of this algorithm surpassed various algorithms in the YOLO series, including YOLOv3, YOLOv5l, YOLOv5m, and YOLOv8s. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.