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Article

Object Detection Using Clustering Algorithm Adaptive Searching Regions in Aerial Images

Authors:

Xi ZhaoAuthors Info & Claims

Computer Vision – ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings, Part IV

Pages 651 - 664

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

Published: 23 August 2020 Publication History

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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Cited By

leng jYe YMO MGao CGan JXiao BGao X(2024)Recent Advances for Aerial Object Detection: A SurveyACM Computing Surveys10.1145/3664598Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3664598
Sarkar AJacobs NVorobeychik YOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)A partially supervised reinforcement learning framework for visual active searchProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666659(12245-12270)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666659
Chen YZhuang JFang H(2023)Object Detection Using Scalable Feature Maps in Remote Sensing ImagesProceedings of the 2023 6th International Conference on Algorithms, Computing and Artificial Intelligence10.1145/3639631.3639634(11-16)Online publication date: 22-Dec-2023
https://dl.acm.org/doi/10.1145/3639631.3639634

Index Terms

Object Detection Using Clustering Algorithm Adaptive Searching Regions in Aerial Images
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Guide Proceedings

Computer Vision – ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings, Part IV

Aug 2020

776 pages

ISBN:978-3-030-66822-8

DOI:10.1007/978-3-030-66823-5

Editors:
Adrien Bartoli
University of Clermont Auvergne, Clermont Ferrand, France
,
Andrea Fusiello
Università degli Studi di Udine, Udine, Italy

© Springer Nature Switzerland AG 2020.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 23 August 2020

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Cited By

leng jYe YMO MGao CGan JXiao BGao X(2024)Recent Advances for Aerial Object Detection: A SurveyACM Computing Surveys10.1145/3664598Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3664598
Sarkar AJacobs NVorobeychik YOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)A partially supervised reinforcement learning framework for visual active searchProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666659(12245-12270)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666659
Chen YZhuang JFang H(2023)Object Detection Using Scalable Feature Maps in Remote Sensing ImagesProceedings of the 2023 6th International Conference on Algorithms, Computing and Artificial Intelligence10.1145/3639631.3639634(11-16)Online publication date: 22-Dec-2023
https://dl.acm.org/doi/10.1145/3639631.3639634

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