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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Rethinking Classification of Oriented Object Detection in Aerial Images

Author 1: Phuc Nguyen
Author 2: Thang Truong
Author 3: Nguyen D. Vo
Author 4: Khang Nguyen

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130987

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 9, 2022.

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Abstract: With the help of the rapid development of technology, especially the prevalence of UAVs (unmanned aerial vehicles), object detection in aerial images gains much more attention in computer vision and deep learning. However, traditional methods use horizontal bounding boxes for object representation leading to inconsistency between objects and features. Therefore, many detectors are being built to tackle this problem, and normally they use the conventional approaches of training and testing to achieve the results. Our pipeline proposed to strengthen not only the classification but also localization via independent training processes using convex-hull transformation in data pre-processing phase. We experimented with the well-designed S2ANet, R3Det, ReDet, RoI Transformer and Oriented R-CNN on the well-established oriented object detection dataset DOTA. Then we adopt the best detectors with the well-known classification network EfficientNet to our proposed pipeline and achieve promising results on the oriented object detection DOTA dataset. Moreover, our pipeline can flexibly be adapted to various oriented object detection baselines improving the results in classification via independent extensive training cycles.

Keywords: Aerial images; classification; convex-hull transformation; data processing; oriented object detection

Phuc Nguyen, Thang Truong, Nguyen D. Vo and Khang Nguyen, “Rethinking Classification of Oriented Object Detection in Aerial Images” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130987

@article{Nguyen2022,
title = {Rethinking Classification of Oriented Object Detection in Aerial Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130987},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130987},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {Phuc Nguyen and Thang Truong and Nguyen D. Vo and Khang Nguyen}
}


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