YOLOX-Drone: An Improved Object Detection Method for UAV Images

Unmanned aerial vehicles (UAV) are widely used for their small size and flexibility. However, the large number of small objects and the significant difference in object size in UAV images bring great challenges to the detection task. Therefore, we propose an object detection method for UAV images with four improvements on the strong baseline model YOLOX-S, which is robust to detect small objects and multi-scale objects. Firstly, we introduce a high-resolution feature map to retain rich detailed information about small objects. Secondly, we propose new up-sampling and down-sampling modules to reduce the feature information loss during the sampling process. Thirdly, we present the triple-scale feature fusion module (TSFFM) to fuse more abundant multi-scale features in the neck’s bottom-up feature fusion process. Finally, the parrell dilated convolution attention module (PD-CAM) is proposed to learn the multi-receptive field features. Experiment results on the VisDrone-VID2019 dataset validate the effectiveness and superiority of the proposed method.