Anchor-free Oriented Proposal Generator for Object Detection

TL;DR

This paper introduces an anchor-free oriented proposal generator for object detection in remote sensing images, improving accuracy by directly predicting oriented boxes without relying on horizontal box intermediates.

Contribution

The paper proposes a novel anchor-free oriented proposal generator that refines coarse oriented boxes, and releases a new dataset to address data scarcity in oriented object detection.

Findings

Achieves 64.41% mAP on DIOR-R dataset

Outperforms existing methods on DOTA and HRSC2016 datasets

Demonstrates effectiveness of anchor-free approach in oriented detection

Abstract

Oriented object detection is a practical and challenging task in remote sensing image interpretation. Nowadays, oriented detectors mostly use horizontal boxes as intermedium to derive oriented boxes from them. However, the horizontal boxes are inclined to get small Intersection-over-Unions (IoUs) with ground truths, which may have some undesirable effects, such as introducing redundant noise, mismatching with ground truths, detracting from the robustness of detectors, etc. In this paper, we propose a novel Anchor-free Oriented Proposal Generator (AOPG) that abandons horizontal box-related operations from the network architecture. AOPG first produces coarse oriented boxes by a Coarse Location Module (CLM) in an anchor-free manner and then refines them into high-quality oriented proposals. After AOPG, we apply a Fast R-CNN head to produce the final detection results. Furthermore, the shortage of large-scale datasets is also a hindrance to the development of oriented object detection. To alleviate the data insufficiency, we release a new dataset on the basis of our DIOR dataset and name it DIOR-R. Massive experiments demonstrate the effectiveness of AOPG. Particularly, without bells and whistles, we achieve the accuracy of 64.41%, 75.24% and 96.22% mAP on the DIOR-R, DOTA and HRSC2016 datasets respectively. Code and models are available at https://github.com/jbwang1997/AOPG.