Research on YOLOv3 algorithm based on darknet framework

Abstract

Abstract Traffic jams and accidents occur frequently in modern cities. In the context of smart cities, intelligent transportation can be effectively controlled through target detection technology. In view of the problems of slow detection speed and low accuracy of traditional vehicle detection algorithms, a YOLOv3 algorithm based on K-means ++ is proposed. The accuracy of bounding box detection is improved by the K-means ++ algorithm. Compared with the traditional YOLOv3 detection algorithm, the improved algorithm improves the detection speed and accuracy. Experiments show that the improved algorithm has a higher recognition rate for small targets in the actual test, while reducing the false detection rate and improving the accuracy of the algorithm.