Rotated Hybrid Task Cascade Network for Remote Sensing Aircraft Target Recognition

Automatic aircraft target recognition, including direction detection and fine-grained classification, is an important but challenging problem. Multi-directional densely arranged targets and the tiny differences between classes cause difficulties in recognition and direction prediction. To overcome the aforementioned problems, a rotated hybrid task cascade (RHTC) network is proposed. Specifically, RHTC cascades the segmentation branch and the bounding-box (bbox) branch to fuse the semantic feature in a coarse- to- fine manner. In addition, a new oriented bounding box regressor (OBBR) is proposed to predict the direction of target, and a new directionalloss function is added to further optimize the regressor. Moreover, we design fine masks in preprocessing to achieve improved recognition performance. The experimental results evaluated on the datasets collected from Google Earth show that RHTC can achieve the state-of-the-art performance on self-defined direction precision (DP) and mean average precision (mAP).