A Study on Human Recognition Using General RGB and Thermal Imaging Cameras in Low-Light Environments

This paper addresses the problem that RGB cameras have a relatively low recognition rate for finding people in low-light environments, and aims to use a them1al imaging camera simultaneously to increase the recognition rate for finding people. For this purpose, a bounding box is created based on what is found in the thermal image using the YOL0v8 model, and the boundary line is extracted using the Canny Edge detection algorithm. Then, by calculating the displacement between the border of the high-intensity image and the border of the thermal image, the error of the RGB camera and the them1al imaging camera is shifted upward. Then, adjust the fineness of the boundary line based on the military value of the Canny Edge detection algorithm. As a result, compared to the initial low-light image, the final low-light image with the border selected had a human recognition rate of 22% on average.