Vehicle detection is an important research direction for intelligent transportation systems. In terms of vehicle detection from the monitoring perspective, a vehicle detection method based on an improved YOLOX algorithm is proposed. The YOLOX_S model with a smaller network depth is used to improve the network structure. The GHOST depthwise separable convolution module is adopted to replace some traditional convolutions, and model parameters are reduced with the model detection accuracy ensured. The CBAM attention module is integrated into a feature extraction network, and a feature enhancement structure is added to enhance the semantic information of feature maps obtained by the network and strengthen the ability of the network in detecting targets. By using the CIoU_loss to optimize the loss function, this study finds that the positioning accuracy of the bounding box of the model is improved. The test results show that the detection accuracy of the improved network is increased by 2.01%, reaching 95.45%, which proves the feasibility of the improved method.