LE QUY DON
Technical University
VietnameseClear Cookie - decide language by browser settings

An Approach of Generating Synthetic Data in Military Camouflaged Object Detection

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.

Full text not available from this repository. (Upload)

Abstract

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)
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

Actions (login required)

View Item
View Item