Vehicle target detection is an important link in intelligent transportation systems. Aiming at the problems of low efficiency, poor detection effect of small targets and high miss rate of traditional vehicle target detection methods, a vehicle target detection algorithm based on the improved YOLOv3 network is proposed. In order to improve the efficiency of vehicle detection, the lightweight model MobileNetv2 is used to replace the feature extraction network in the original YOLOv3, and the calculation amount is reduced compared with the original algorithm. In order to effectively improve the network's ability to detect small-scale vehicle targets, feature layers of different scales are fused and target detection is carried out on feature maps of different scales. At the same time, in order to obtain more abundant semantic feature information and improve the prediction ability of network, a feature enhancement module is proposed. For the specific application of vehicle target detection, K-means is used to re-cluster anchor frames to meet the requirements of vehicle target detection. Combined with the above improvements, the vehicle target detection network YOLOv3-M2 is obtained. Experimental results show that compared with YOLOv3, the proposed method not only improves the detection efficiency, but also improves the small target detection capability, increasing the average detection accuracy of the network by about 9%.