P‐2.1: Research on real‐time performance of road condition display

Abstract

Road target detection and recognition is of great significance in the current field of automatic driving, and in the road target detection and recognition, the high precision of detection algorithm and fast reasoning speed are very important for safe automatic driving. The YOLOv5 (YOLO, you only look once) target detection algorithm can be used to identify and analyze road targets on the way of vehicles, playing a role in assisting driving and reducing safety risks. By using the improved YOLOv5 target detection algorithm to train the BDD100K dataset, the model obtained can significantly improve the recall rate and thus the accuracy. The improved YOLOv5 algorithm mainly uses K‐means algorithm to find the most appropriate anchors for data sets, and gets more accurate models through real‐time data augmentation training. The results show that, on the BDD100K test set, the MAP‐50 of the improved model can reach 51.8%, and compared with the performance of the original model, the improved model has significantly improved the target detection mAP. Compared with the manual model, the proposed model can detect the target more accurately while guaranteeing the detection speed.