Knowledge distillation-based approach for object detection in thermal images during adverse weather conditions

In today’s technology landscape, systems must adapt to diverse conditions to be practically useful. Thermal imaging’s intersection with adverse weather presents a challenge for existing heavy networks designed for RGB images. This research addresses this gap by using knowledge distillation to optimise networks for thermal imaging in challenging weather. Current networks struggle with interpreting thermal images effectively in adverse conditions like fog or rain. Through knowledge distillation, our work aims to enhance these networks, ensuring compatibility and efficiency with thermal imaging. This effort holds promise for enhancing object detection in thermal images during adverse weather, benefiting surveillance systems, improving safety in self-driving vehicles under harsh conditions, and aiding search and rescue operations with limited visibility. This research doesn’t just refine networks; it empowers technology to excel in adverse conditions, promising practical applications that enhance safety, efficiency, and reliability across various technological domains.