Abstract: Aiming at the problems of high missed detection rate and low detection accuracy of YOLOv3 in road target detection，this paper proposed a road target detection method based on improved YOLOv3. By increasing the three feature scales of the original YOLOv3 to four，the object accuracy for small targets was improved. Secondly，we used the CIoU loss function to improve the model's accuracy and the K-Means++ clustering algorithm to recluster the road targets to get new candidate boxes. This paper verified the effect of the improved YOLOv3 algorithm on the BDD100K data set. The experimental results showed that the improved YOLOv3 algorithm had achieved good detection results in reducing the missed detection rate and improving the detection accuracy.

Key words: road target detection, YOLOv3, K-Means++