UAV-YOLOX: An Accurate Object Detection Method for UAV Images.

Object detection for Unmanned Aerial Vehicle (UAV) images has become a hotspot in the field of computer vision. Accurate object detection is significant for abundant UAV utilities. However, tiny objects and complex backgrounds in UVA images challenge the performance of object detection. To address the challenges, in this paper, we propose an accurate object detection method for UAV images, named UAV-YOLOX, by proposing some improvements to modify the state-of-the-art YOLOX detector. First, an Aug-Rotation data enhancement method is used to enhance the rotation invariance of the convolutional neural network, by alleviating inconsistent angles of similar objects in the dataset caused by different shooting angles of UAV. Then, a weighted cyclic feature pyramid is designed to improve the performance of object detection, by avoiding the loss of high-level semantic features when fusing features in a traditional feature pyramid structure. Finally, in the part of prediction head, this paper proposes an attention head structure, which regards the regression and classification of the bounding box as two tasks. The prediction head based on the attention mechanism can learn different features for different tasks. Experimental results on the public datasets and collected datasets show that the proposed method achieves much higher accuracy.