Multi-object detection method for vehicles based on improved YOLOv3 model

Abstract

To solve the problems of low detection rate and poor robustness of near and far object on real road environment, YOLOv3-Y based on the Darknet-53 feature extraction network model was proposed. The 8×down-sampling feature map and the 4×down-sampling feature map output by the network were spliced to create a new detection layer of 104×104 scale. The K-means algorithm was used to cluster the vehicle dataset including four categories, and 12 anchors were selected and matched them to four detection layers with different scales respectively. Meanwhile, GIOU loss function was introduced to optimize the intersection-over-union(IOU)loss function. The actual vehicle dataset in the laboratory was used to compare YOLOv3-Y with YOLOv2, YOLOv2-voc, YOLOv2-tiny, YOLOv3 and YOLOv3-tiny models. The results show that the average precision and recall rate of the YOLOv3-Y model are significantly better than the above algorithms, and the minimum improvement values are 11.05% and 5.20%, respectively.

Keywords

• deep learning
• multi-object detection
• yolov3 model
• k-means clustering algorithm
• giou loss function