AOGC: Anchor-Free Oriented Object Detection Based on Gaussian Centerness

Abstract

Oriented object detection is a challenging task in scene text detection and remote sensing image analysis, and it has attracted extensive attention due to the development of deep learning in recent years. Currently, mainstream oriented object detectors are anchor-based methods. These methods increase the computational load of the network and cause a large amount of anchor box redundancy. In order to address this issue, we proposed an anchor-free oriented object detection method based on Gaussian centerness (AOGC), which is a single-stage anchor-free detection method. Our method uses contextual attention FPN (CAFPN) to obtain the contextual information of the target. Then, we designed a label assignment method for the oriented objects, which can select positive samples with higher quality and is suitable for large aspect ratio targets. Finally, we developed a Gaussian kernel-based centerness branch that can effectively determine the significance of different anchors. AOGC achieved a mAP of 74.30% on the DOTA-1.0 datasets and 89.80% on the HRSC2016 datasets, respectively. Our experimental results show that AOGC exhibits superior performance to other methods in single-stage oriented object detection and achieves similar performance to the two-stage methods.