Driving vehicle elements detection method based on optimized YOLOv4 algorithm

The rapid increase in the number of vehicles also brings a series of management problems. The intelligent transportation system is an effective solution. Due to the traditional target recognition method was greatly affected by factors such as weather, distance, angle, and illumination, and the accuracy of the information detection of driver's face, hand which was based on the original YOLOv4 algorithm was not high, a detection and positioning method based on the optimized YOLOv4 algorithm was proposed. While adding a smaller detection scale to the original YOLOv4 network, a fuzzy ISODATA dynamic clustering algorithm was used to optimize the number of a priori frames, and the experiments using the real intersection data set were carried out. The experimental results show that the optimized network has an average accuracy of 98.56% between classes in the training set and a detection frame rate of 41.43, which are higher than those of the original network.