Enhancing License Plate Recognition in Foggy Condition Base on Yolov8 and Dark Channel Prior

This paper proposes a novel approach that combines the YOLOv8 object detection model with the Dark Channel Prior (DCP) algorithm to improve car license plate detection in foggy weather conditions. The proposed approach consists of two stages: the first stage is data preprocessing with the DCP approach, and the second stage is image preprocessing. The DCP model is trained to detect the license plates in the enhanced images, providing the final output of the proposed method. The experimental findings show that the suggested strategy significantly boosts detection accuracy and improves license plate visibility compared to conventional methods. Moreover, the integration of DCP with Yolov8 resulted in robust performance against fog, achieving an average precision of 0.975.