Vehicle information detection is the primary task of vehicle type identification in the field of intelligent transportation. Based on deep learning YOLOv3 (You Only Look Once Version 3) model, a new YOLOv3-fass object detection algorithm was proposed to address some problems existing in vehicle information detection technology such as detection speed, accuracy and stability. In this improved algorithm, based on DarkNet-53 network structure, some residual structures were deleted, and a number of channels of convolutional layer were reduced; a down-sampling branch, three scale-hopping connection structures, and one detection scale were added; and twelve groups of anchor frame values were calculated through the means of K-means clustering algorithm combined with manual setting. Finally, YOLOv3-fass algorithm was fine-tuned through the migration learning mechanism of multi-stage pre-training. YOLOv3-fass algorithm was compared with YOLOv3, YOLOv3-tiny, YOLOv3-spp and two algorithms with ResNet50 and DenseNet201 on the vehicle data set. The experimental results show that YOLOv3-fass algorithm can detect vehicle information more accurately, efficiently and stably.