Contrast Limited Adaptive Histogram Equalization for Recognizing Road Marking at Night Based on Yolo Models

In recent years, artificial intelligence has led to rapid development and application across various industries. One of the significant developments is the improvement of transportation methods. Accidents involving vehicles frequently result in a high number of fatalities as well as economic damage. Road detection is one of the applications that can be used by self-driving cars. Traffic accidents happen, but artificial intelligence is used in many nations to construct smart cities and apps for self-driving cars. Since public road sign datasets have been used in significant research for road sign identification and analysis, these datasets are particularly significant for training autonomous vehicles. This study records the roads of various cities in Taiwan through road driving. It manually collects traffic signs in Taiwan to create a data set of road signs in Taiwan in daytime environments as well as nighttime environments. Since there is currently no data set of road signs in Taiwan, this study is necessary to create a data set of road signs in Taiwan. The YOLO model is utilized in this work to design road signs in Taiwan for mark detection. The techniques of Contrast Stretching (CS), Histogram Equalization (HE), and Contrast Limited Adaptive Histogram Equalization (CLAHE) are evaluated in a nighttime setting and compared to the original image captured at night. The experimental results show that the best model during the day is YOLO V4 (no flip), the test set mAP is 86.77%, the Precision is 82%, the Recall is 87%, the F1-score is 84%, and the IoU is 63.92%. At night, the CLAHE image method works best in the YOLOv5x model, with a mAP of 86.40%. YOLOv5 can be used in mobile devices or embedded devices, so this study recommends using CLAHE’s YOLOv5x as the best model at night and used to improve the effect of road sign detection at night.