Truong, T.T.H. and Kien Tran, T. and Nguyen, C.T. and Hai Hong Phan, T. and Nguyen, A.T. and Nguyen, N.S. (2024) An Approach of Generating Synthetic Data in Military Camouflaged Object Detection. In: 7th International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2024, 15 August 2024 Through 16 August 2024, Da Nang.
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Camouflaged object detection is paramount across diverse domains, spanning military surveillance, wildlife conservation, and security systems. Despite the efficiency of deep learning in military camouflage detection, significant challenges persist, notably the scarcity of training data. This study addresses these challenges by presenting a pioneering method to generate synthetic camouflage images from 3D models, specifically designed for detecting camouflage in military settings. Additionally, we introduce a robust camouflage assessment method to augment the quality of synthetic data. Through extensive experimentation, we validate that data generated under optimal conditions substantially boosts the performance of state-of-the-art object detection models in identifying camouflaged objects. This research thus offers a promising avenue for advancing the effectiveness and applicability of camouflage detection systems across various real-world scenarios. © 2024 IEEE.
Item Type: | Conference or Workshop Item (Paper) |
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Divisions: | Faculties > Faculty of Information Technology |
Identification Number: | 10.1109/MAPR63514.2024.10660900 |
Uncontrolled Keywords: | 3D models; 3d synthetic data; 3d-modeling; Diverse domains; Military camouflaged object detection; Military surveillance; Objects detection; Synthetic data; Training data; Wildlife conservation |
Additional Information: | Conference of 7th International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2024 ; Conference Date: 15 August 2024 Through 16 August 2024; Conference Code:202522 |
URI: | http://eprints.lqdtu.edu.vn/id/eprint/11409 |