Aiming at the low detection efficiency and accuracy of existing deep learning vehicle target detection algorithms, a VOD-YOLOv5 vehicle target detection algorithm was proposed based on the YOLOv5 model. In this paper, a new lightweight convolutional neural module (cf) is proposed to improve the feature extraction capability of the network. At the same time, the attention mechanism of space and channel fusion is integrated into the network model, which improves the detection accuracy of small and medium-sized targets in fuzzy images. The experimental results show that compared with the original YOLOv5 model, the VOD-YOLOv5 model proposed in this paper has a 4% increase in average accuracy (mAP), and the average accuracy (AP) of detection of different target classes has been improved, and the detection speed meets the real-time requirements, effectively improving the detection performance of the vehicle target detection model.