FAA-Det: Feature Augmentation and Alignment for Anchor-Free Oriented Object Detection

Abstract

Oriented object detection with remote sensing scenes has made excellent progress in recent years, especially using anchor-free detectors. Without the limitation of inherent prior spatial information, anchor-free detectors regress the detection boxes from the object center or edge in an elegant way. However, anchor-free detectors suffer severe feature misalignment and inconsistency between classification and regression. Especially in remote sensing scenes, there are densely arranged instances and multi-scale representations, which will affect the detection accuracy. Therefore, a feature augmentation module (FAM) and an oriented feature alignment (OFA) module are proposed for oriented object detection called FAA-Det. More specifically, we first introduce a FAM to enhance the object representation. After that, the augmented feature maps will be fed into OFA for feature alignment and accurate detection. OFA has two independent branches for classification and regression, and their separate structures can alleviate the inconsistency in detection. FAM and OFA comprise the FAA-Head in our detector. Extensive evaluation demonstrates the effectiveness of our proposed FAA-Det that performs the state-of-the-art (SOTA) mean average precision (mAP) on the DOTA and HRSC2016 datasets without bells and whistles. Our code will be available at https://github.com/jimuIee/FAA-Det .