Aspect-Ratio-Guided Detection for Oriented Objects in Remote Sensing Images
Caiguang Zhang, Boli Xiong, Xiao Li, Gangyao Kuang
Abstract
Although existing oriented object detection methods have made considerable progress based on oriented heads or anchors, the training process itself is not perfect. In this letter, we point out the inconsistency problem between the fixed network setting and varying aspect ratios, which greatly limits the performance. For example, the fixed parameters in label assignment and regression loss cannot fit the changes of aspect ratios and, thus, are harmful to the training process. Considering the prior information about objects’ aspect ratios, the aspect-ratio-guided (ARG) methods are proposed. Specifically, the ARG label assignment is used to adjust the label assignment criteria (intersection over union (IoU) threshold) automatically, and the ARG IoU loss can change the weights of angle regression dynamically. This ARG design makes better use of training samples and pushes the detector more robust to the change of aspect ratios. With no additional cost, our method improves upon the ResNet-50-feature pyramid network (FPN) baseline with 3.99% AP50 and 6.09% AP75 on HRSC2016.