INPUT IMAGE ENHANCEMENT FOR VEHICLE DETECTION UNDER LOW ILLUMINANCE CONDITIONS

Abstract

A method of detecting objects based on images has been utilized to detect vehicles in a parking lot. It is expected to realize optimized operation of parking lots by simulation on the digital twin constructed based on vehicle information obtained from images. However, the accuracy of the vehicle detection method based on images deteriorates significantly under low illuminance conditions. This is due to the decrease in contrast and the noise generated by the image sensor. Low quality images containing these factors are accumulated under low illuminance conditions. In this paper, we propose a method that uses multiple images clarified by applying noise reduction and contrast enhancement as preprocessing for vehicle detection under low illuminance conditions. The proposed method applies object detection using deep learning to these preprocessed images and determines the parking status of the vehicle compartment. Then, the number of detection omissions is reduced by applying the logical OR to the obtained results. Experiments using YOLOv4 for object detection confirmed the effectiveness of the proposed method.