  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%.
