Abstract:

The targets camouflage， targets occlusion， moving dodge and fake targets deteriorate the performance of UAV object detection and tracking， and the shadow regions around UAV target aggravate the negative effect of these factors. Thus， shadow detection is an important task for UAV. The shadow detection of UAV target suffering from limited training images， difficulty in labeling ground truth data and mass of tiny shadow regions. To deal with these problems， we propose a UAV target shadow detection method based on residual mixed supervision network. Firstly， we design a resolution-aware attention shadow detection network based on the character of shadow regions in UAV target. The newly designed network can maintain the lower texture feature more accurately. Then we design mixed supervision network to enlarge the number of training images. The teacher network is trained by both ordinary dataset and UAV dataset， while the student network is trained based on UAV dataset and the parameter of teacher network. Meanwhile we design residual images to further enlarge the number of training images and makes the network pay more attention to tiny shadow regions. The residual image is calculated by measuring the difference between detection results of teacher network and ground truth data. At last， the proposed method is compared with existing methods on two public UAV target shadow detection datasets. The evaluation metrics are improved by 41.6% at most. The experiment proves the effectiveness and accuracy of proposed shadow detection method on UAV target.

Key words: UAV target, shadow detection, mixed supervision network, residual images, resolution-aware attention network