Thermal-Infrared Remote Target Detection System for Maritime Rescue based on Data Augmentation with 3D Synthetic Data

• URL: http://arxiv.org/abs/2310.20412v1
• Date: Tue, 31 Oct 2023 12:37:49 GMT
• Title: Thermal-Infrared Remote Target Detection System for Maritime Rescue based on Data Augmentation with 3D Synthetic Data
• Authors: Sungjin Cheong, Wonho Jung, Yoon Seop Lim, Yong-Hwa Park
• Abstract summary: This paper proposes a thermal-infrared (TIR) remote target detection system for maritime rescue using deep learning and data augmentation. To address dataset scarcity and improve model robustness, a synthetic dataset from a 3D game (ARMA3) to augment the data is collected. The proposed segmentation model surpasses the performance of state-of-the-art segmentation methods.
• Score: 4.66313002591741
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper proposes a thermal-infrared (TIR) remote target detection system for maritime rescue using deep learning and data augmentation. We established a self-collected TIR dataset consisting of multiple scenes imitating human rescue situations using a TIR camera (FLIR). Additionally, to address dataset scarcity and improve model robustness, a synthetic dataset from a 3D game (ARMA3) to augment the data is further collected. However, a significant domain gap exists between synthetic TIR and real TIR images. Hence, a proper domain adaptation algorithm is essential to overcome the gap. Therefore, we suggest a domain adaptation algorithm in a target-background separated manner from 3D game-to-real, based on a generative model, to address this issue. Furthermore, a segmentation network with fixed-weight kernels at the head is proposed to improve the signal-to-noise ratio (SNR) and provide weak attention, as remote TIR targets inherently suffer from unclear boundaries. Experiment results reveal that the network trained on augmented data consisting of translated synthetic and real TIR data outperforms that trained on only real TIR data by a large margin. Furthermore, the proposed segmentation model surpasses the performance of state-of-the-art segmentation methods.

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