  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.
