Experiments on detecting and monitoring objects based on thermal imaging

Object detection and tracking on RGB and thermal imaging is getting increasingly more advanced and important in the field of computer vision. The increasing image quality combined with decreasing camera prices during recent years, created new opportunities to gather data. They are now used in many different fields, for example: medical, military, law enforcement or industrial applications. Thermal cameras are useful in darkness and in situations when there is not enough light for cameras that operate in the visual spectrum to function properly. Leveraging the information provided by colored images for labeling, this paper approach aims to enhance the accuracy and efficiency of object recognition in thermal imagery. The synergy between thermal and colored imagery offers a comprehensive solution, addressing challenges posed by limited thermal resolution and ambiguity in object boundaries. By harnessing the complementary strengths of colored and thermal imaging, this paper opens avenues for applications in surveillance, autonomous systems, and other fields where reliable object detection and tracking under challenging thermal conditions are paramount. For this experiment different models were trained using MMDetection and MMRotate modules from MMLab framework. We explored and compared multiple models like: Rotated Faster R-CNN (two-stage anchor-based detection), Single Shot Alignment Network (S2ANET one stage anchor detector), and FCOSR (one-stage anchor-free detector)