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.

翻译：本文提出了一种基于深度学习与数据增强的热红外（TIR）远程目标检测系统，用于海上救援任务。我们利用热红外相机（FLIR）模拟了多种人体救援场景，构建了自采集的TIR数据集。此外，为解决数据集稀缺性并提升模型鲁棒性，我们进一步收集了来自三维游戏（ARMA3）的合成数据集以增强数据。然而，合成TIR图像与真实TIR图像之间存在显著的域差异。因此，引入合适的域自适应算法至关重要。为此，我们提出了一种基于生成模型的、以目标-背景分离方式实现从三维游戏到真实场景的域自适应算法，以解决该问题。同时，针对远程TIR目标固有边界模糊的特性，本文提出了一种头部采用固定权重核的分割网络，以提升信噪比（SNR）并提供弱注意力机制。实验结果表明，使用由翻译合成数据与真实TIR数据组成的增强数据集进行训练的网络，其性能显著优于仅使用真实TIR数据训练的网络。此外，所提出的分割模型超越了当前最优分割方法的性能。