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A Light-Weight Object Detection Framework with FPA Module for Optical Remote Sensing Imagery

Authors:

Xi Gu,

Lingbin Kong,

Zhicheng Wang,

Jie Li,

Zhaohui Yu,

Gang WeiAuthors Info & Claims

HPCCT & BDAI '20: Proceedings of the 2020 4th High Performance Computing and Cluster Technologies Conference & 2020 3rd International Conference on Big Data and Artificial Intelligence

Pages 109 - 115

https://doi.org/10.1145/3409501.3409545

Published: 25 August 2020 Publication History

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Abstract

With the development of remote sensing technology, the acquisition of remote sensing images is easier and easier, which provides sufficient data resources for the task of detecting remote sensing objects. However, how to detect objects quickly and accurately from many complex optical remote sensing images is a challenging hot issue. In this paper, we propose an efficient anchor free object detector, CenterFPANet. To pursue speed, we use a lightweight backbone and introduce the asymmetric revolution block. To improve the accuracy, we design the FPA module, which links the feature maps of different levels, and introduces the attention mechanism to dynamically adjust the weights of each level of feature maps, which solves the problem of detection difficulty caused by large size range of remote sensing objects. This strategy can improve the accuracy of remote sensing image object detection without reducing the detection speed. On the DOTA dataset, CenterFPANet mAP is 64.00%, and FPS is 22.2, which is close to the accuracy of the anchor-based methods currently used and much faster than them. Compared with Faster RCNN, mAP is 6.76% lower but 60.87% faster. All in all, CenterFPANet achieves a balance between speed and accuracy in large-scale optical remote sensing object detection.

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Wang XChen HChu XWang P(2024)AODet: Aerial Object Detection Using Transformers for Foreground RegionsIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.340781562(1-11)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3407815
Shi TGong JJiang SZhi XBao GSun YZhang W(2023)Complex Optical Remote-Sensing Aircraft Detection Dataset and BenchmarkIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2023.328313761(1-9)Online publication date: 2023
https://doi.org/10.1109/TGRS.2023.3283137
Dang LHuangfu PHou YLiu YHan H(2023)A Path Aggregation Network Based on Residual Feature Enhancement for Object Detection in Remote Sensing ImageryRemote Sensing Letters10.1080/2150704X.2023.222179414:6(598-608)Online publication date: 13-Jun-2023
https://doi.org/10.1080/2150704X.2023.2221794
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Index Terms

A Light-Weight Object Detection Framework with FPA Module for Optical Remote Sensing Imagery
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection

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Published In

HPCCT & BDAI '20: Proceedings of the 2020 4th High Performance Computing and Cluster Technologies Conference & 2020 3rd International Conference on Big Data and Artificial Intelligence

July 2020

276 pages

ISBN:9781450375603

DOI:10.1145/3409501

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Xi'an Jiaotong-Liverpool University: Xi'an Jiaotong-Liverpool University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 August 2020

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HPCCT & BDAI 2020

HPCCT & BDAI 2020: 2020 4th High Performance Computing and Cluster Technologies Conference & 2020 3rd International Conference on Big Data and Artificial Intelligence

July 3 - 6, 2020

Qingdao, China

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Cited By

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Wang XChen HChu XWang P(2024)AODet: Aerial Object Detection Using Transformers for Foreground RegionsIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.340781562(1-11)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3407815
Shi TGong JJiang SZhi XBao GSun YZhang W(2023)Complex Optical Remote-Sensing Aircraft Detection Dataset and BenchmarkIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2023.328313761(1-9)Online publication date: 2023
https://doi.org/10.1109/TGRS.2023.3283137
Dang LHuangfu PHou YLiu YHan H(2023)A Path Aggregation Network Based on Residual Feature Enhancement for Object Detection in Remote Sensing ImageryRemote Sensing Letters10.1080/2150704X.2023.222179414:6(598-608)Online publication date: 13-Jun-2023
https://doi.org/10.1080/2150704X.2023.2221794
Liu JLi SZhou CCao XGao YWang B(2022)SRAF-Net: A Scene-Relevant Anchor-Free Object Detection Network in Remote Sensing ImagesIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2021.312495960(1-14)Online publication date: 2022
https://doi.org/10.1109/TGRS.2021.3124959

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