Anchor-Free Oriented Proposal Generator for Object Detection

doi:10.1109/TGRS.2022.3183022

2022-01-01

204

DOI：10.1109/TGRS.2022.3183022

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摘要

En 中文

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, and detracting from the robustness of detectors. In this article, 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 Region-based Convolutional Neural Network (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.

Keyword:

Proposals

Detectors

Object detection

Generators

Feature extraction

Remote sensing

Shape

Anchor-free oriented proposal generator (AOPG)

oriented object detection

oriented proposal generation

期刊

IEEE Transactions on Geoscience and Remote Sensing

IF:

8.6

论文数:

2.1W

被引数:

10.7W

机构

pla information engineering university

学者数:

2.8K

论文数: 1.6K

被引数: 2

Northwestern Polytechnical University

学者数:

4.6W

论文数: 3.7W

被引数: 5.3W

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