With the rapid development of social economy, transportation has become faster and more efficient. As an important part of goods transportation, the safe maintenance of tunnel highways has become particularly important. The maintenance of tunnel roads has become more difficult due to problems such as sealing, narrowness and lack of light. Currently, target detection methods are advantageous in detecting tunnel vehicles in a timely manner through monitoring. Therefore, in order to prevent vehicle misdetection and missed detection in this complex environment, we propose a YOLOv5-Vehicle model based on the YOLOv5 network. This model is improved in three ways. Firstly, The backbone network of YOLOv5 is replaced by the lightweight MobileNetV3 network to extract features, which reduces the number of model parameters; Next, all convolutions in the neck module are improved to the depth-wise separable convolutions to further reduce the number of model parameters and computation, and improve the detection speed of the model; Finally, to ensure the accuracy of the model, the CBAM attention mechanism is introduced to improve the detection accuracy and precision of the model. Experiments results demonstrate that the YOLOv5-Vehicle model can improve the accuracy.