Improved object detection algorithms for optical aerial images based on region proposal network

Yuxing Lin; Liaoni Wu; Bo Huang; Boyong He

doi:10.1117/12.2604541

5 October 2021 Improved object detection algorithms for optical aerial images based on region proposal network

Yuxing Lin, Liaoni Wu, Bo Huang, Boyong He

Proceedings Volume 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning; 1191104 (2021) https://doi.org/10.1117/12.2604541
Event: 2nd International Conference on Computer Vision, Image and Deep Learning, 2021, Liuzhou, China

Abstract

In this paper, we firstly propose that the region proposal network has some defects, such as effect depending on preset parameters, and poor adaptability to different tasks. Secondly, we integrate the idea of adaptive training sample selection into the region proposal network to define samples adaptively and add a centerness branch to constrain low-quality anchors. In the meanwhile, we try to merge the branch structure before classification and regression tasks to further improve the performance of ATSS_RPN. As demonstrated in our experiment, the proposed ATSS_RPN gets a 7.5% higher recall than the origin RPN. We also verify our scheme through two classic optical aerial image datasets. Experiments show that our ATSS_RPN has a considerate performance on the 4 mainstream two-stage detectors(including state of the arts DecteoRS ) than with the origin RPN network. (7% higher mAP in Airbus ship and 1.5% higher mAP in Visdrone2019 when we use Faster RCNN as a baseline).

Citation Download Citation

Yuxing Lin, Liaoni Wu, Bo Huang, and Boyong He "Improved object detection algorithms for optical aerial images based on region proposal network", Proc. SPIE 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning, 1191104 (5 October 2021); https://doi.org/10.1117/12.2604541

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KEYWORDS

Sensors

Detection and tracking algorithms

Target detection

Convolution

Binary data

Lawrencium

Machine learning

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