A paced multi-stage block-wise approach for object detection in thermal images.

The growing advocacy of thermal imagery in applications, such as autonomous vehicles, surveillance, and COVID-19 detection, necessitates accurate object detection frameworks for the thermal domain. Conventional methods could fall short, especially in situations with poor lighting, for instance, detection during night-time. In this paper, we propose a paced multi-stage block-wise framework for effectively detecting objects from thermal images. Our approach utilizes the pre-existing knowledge of deep neural network-based object detectors trained on large-scale natural image data to enhance performance in the thermal domain constructively. The employed, multi-stage approach drives our model to achieve higher accuracies. And the introduction of the pace parameter during domain adaption enables efficient training. Our experimental results demonstrate that the framework outperforms previous benchmarks on the FLIR ADAS dataset on the person, bicycle, and car categories. We have also illustrated further analysis of the framework, such as the effect of its components on accuracy and training efficiency, its generalizability to other thermal datasets, and its superior performance on night-time images in contrast to state-of-the-art RGB object detectors.