A Novel Object Detector Based on High-Quality Rotation Proposal Generation and Adaptive Angle Optimization

Yajun Qiao, Lingjuan Miao, Zhiqiang Zhou, Qi Ming

Abstract: Currently, reliable and accurately oriented detection in remote sensing images still needs to be improved. The wide variation of object shapes and orientations in the remote sensing images usually leads to two issues in two-stage oriented object detectors. One issue is how to generate high-quality rotation proposals. The other is the angular error sensitivity to the aspect ratios in the angle optimization process. In this article, we propose a novel rotation proposal generation and optimization detector, which is based on high-quality rotation proposal generation and adaptive angle optimization to solve these two issues. The proposed method mainly establishes the geometric relationship guided region proposal networks (GRG-RPNs) and the adaptive angle optimization head (AAO-Head) to achieve more accurate oriented object detection. The GRG-RPN only uses a simple network and a small number of horizontal anchors to predict high-quality rotation proposals. This approach was derived via the calculation based on the theoretical analysis of the geometric relationship between the oriented bounding boxes (OBBs) and their external horizontal bounding boxes (EHBBs). The AAO-Head solves the angular error sensitivity to the aspect ratios and achieves adaptive angle optimization using a new regression parameter, which is defined based on the theoretical analysis of the relationship between the intersection over union (IoU), the angular errors, and the aspect ratios. The experiments show that our method can achieve a 2.5% mAP improvement average versus the compared state-of-the-art (SOTA) methods, and achieve 0.5% mAP improvement versus the next best method-oriented regions with CNN features (R-CNN) with fewer regression parameters and a simpler regression approach.