Hotspot detection using thermal imaging has recently become essential in several industrial applications, such as security applications, health applications, and equipment monitoring applications. Hotspot detection is of utmost importance in industrial safety where equipment can develop anomalies. Hotspots are early indicators of such anomalies. We address the problem of hotspot detection in thermal images by proposing a self-supervised learning approach. Self-supervised learning has shown potential as a competitive alternative to their supervised learning counterparts but their application to thermography has been limited. This has been due to lack of diverse data availability, domain specific pre-trained models, standardized benchmarks, etc. We propose a self-supervised representation learning approach followed by fine-tuning that improves detection of hotspots by classification. The SimSiam network based ensemble classifier decides whether an image contains hotspots or not. Detection of hotspots is followed by precise hotspot isolation. By doing so, we are able to provide a highly accurate and precise hotspot identification, applicable to a wide range of applications. We created a novel large thermal image dataset to address the issue of paucity of easily accessible thermal images. Our experiments with the dataset created by us and a publicly available segmentation dataset show the potential of our approach for hotspot detection and its ability to isolate hotspots with high accuracy. We achieve a Dice Coefficient of 0.736, the highest when compared with existing hotspot identification techniques. Our experiments also show self-supervised learning as a strong contender of supervised learning, providing competitive metrics for hotspot detection, with the highest accuracy of our approach being 97%.

翻译：热成像热点检测近年来在安全监控、健康诊断及设备监测等工业应用中变得至关重要。在工业安全领域，设备可能出现异常，而热点正是此类异常的早期征兆。针对热图像中的热点检测问题，本文提出了一种自监督学习方法。自监督学习已被证明可作为监督学习的强有力替代方案，但其在热成像领域的应用仍十分有限，主要原因包括多样性数据匮乏、缺乏领域专用预训练模型以及标准化基准测试不足等。我们提出了一种自监督表征学习方法，结合后续微调流程，通过分类任务提升热点检测性能。基于SimSiam网络的集成分类器可判定图像是否包含热点，并在检测后实现精准的热点隔离。这种方法能够提供高精度、高准确率的热点识别，适用于多种应用场景。为缓解热图像数据集获取困难的问题，我们创建了一个新型大规模热图像数据集。基于自建数据集与公开分割数据集的实验表明，该方法在热点检测与高精度隔离方面具有显著潜力。与现有热点识别技术相比，本方法实现了0.736的Dice系数，达到最高水平。实验同时显示，自监督学习可作为监督学习的强有力竞争者，在热点检测中提供具有竞争力的指标，其中最高准确率达97%。