Small Target Detection Algorithm for UAV Based on Improved YOLOv5

The small and dense objects in unmanned aerial vehicle (UAV) images deteriorate the detection accuracy of neural networks. This article proposed an improved YOLOv5-based algorithm for small and dense object detection in UAV images. To enhance the capability of acquiring the feature information and the receptive field of the network in the backbone feature extraction network, we proposed an enhanced feature extraction (EFE) module, while incorporating the advantages of different pooling methods, and introduced the receptive field block (RFB) module, which realized fusion of different features. Meanwhile, we improved the multi-scale detection module, while enhancing the detection capability of the network for small objects in UAV images. Experiments were done on the VisDrone-DET2019 dataset. The improved algorithm achieved 39.4% mean average precision (mAP), which was 5.5% better than the benchmark network. The experimental results showed that the YOLOv5 algorithm proposed in this article was feasible and effective.