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European Conference on Computer Vision

ECCV 2020: Computer Vision – ECCV 2020 Workshops pp 651–664Cite as

Object Detection Using Clustering Algorithm Adaptive Searching Regions in Aerial Images

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12538))

Abstract

Aerial images are increasingly used for critical tasks, such as traffic monitoring, pedestrian tracking, and infrastructure inspection. However, aerial images have the following main challenges: 1) small objects with non-uniform distribution; 2) the large difference in object size. In this paper, we propose a new network architecture, Cluster Region Estimation Network (CRENet), to solve these challenges. CRENet uses a clustering algorithm to search cluster regions containing dense objects, which makes the detector focus on these regions to reduce background interference and improve detection efficiency. However, not every cluster region can bring precision gain, so each cluster region difficulty score is calculated to mine the difficult region and eliminate the simple cluster region, which can speed up the detection. Then, a Gaussian scaling function(GSF) is used to scale the difficult cluster region to reduce the difference of object size. Our experiments show that CRENet achieves better performance than previous approaches on the VisDrone dataset. Our best model achieved 4.3\(\%\) improvement on the VisDrone dataset.

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Notes

  1. 1.

    The ROI here is different from Faster RCNN [29]. The ROI of this paper contains not just one object but multiple objects of interest, and it is used to represent the region with dense objects.

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Acknowledgements

This work was supported by National Natural Science Foundation of China grant 61573266.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Xidian University, Xi’an, China

    Yi Wang, Youlong Yang & Xi Zhao

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  1. Yi Wang
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  2. Youlong Yang
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  3. Xi Zhao
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Correspondence to Youlong Yang .

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Editors and Affiliations

  1. University of Clermont Auvergne, Clermont Ferrand, France

    Adrien Bartoli

  2. Università degli Studi di Udine, Udine, Italy

    Andrea Fusiello

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Wang, Y., Yang, Y., Zhao, X. (2020). Object Detection Using Clustering Algorithm Adaptive Searching Regions in Aerial Images. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12538. Springer, Cham. https://doi.org/10.1007/978-3-030-66823-5_39

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  • DOI: https://doi.org/10.1007/978-3-030-66823-5_39

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