In the application of Intelligent Transportation, target detection algorithm based on deep learning has the problems of large number of model parameters, slow calculation speed and low accuracy of simple network for vehicle detection. This paper presents an efficient lightweight vehicle detection model, which is improved by using YOLOv4 network as a reference model. First, this paper uses CSPMobileViT network to replace the original backbone network, then replaces PANet with BiFPN, replaces 3×3 standard convolution in BiFPN with deep detachable convolution, and finally adds ECA module before BiFPN and YOLO-Head. In the loss function section, the Border Regression Loss CIoU is improved to Focal EIoU to solve the problem of difficult sample imbalance. The experimental results show that the mAP value of the improved network is 96.77%, the detection speed reaches 0.023 4 s per picture, the model size is only 32.76 MB, and the parameter amount is 8 587 541. Compared with the original algorithm, the mAP is improved by 1.54%, while the model size and number of parameters are only about 1/8 of the original model, and the FPS is improved by 7.5, so the improved algorithm has better detection effect.