Vehicle detection based on deep learning plays a vital role in many fields. In recent years， it has presented a major development direction for computer vision. Lightweight vehicle detection includes the exploration of network structure and computing efficiency， and it is widely used in many fields such as intelligent transportation. However， challenges exist in different scenarios， such as large changes in vehicle scale in detection cameras and vehicles overlapping each other， which reduce the precision of the network in detecting vehicles. To solve these problems， this study proposes an improved YOLOv5s method for detecting vehicles. First， the study proposes to capture long-distance dependencies between objects through a visual attention network and apply new weights to the network’s original feature map to increase the adaptability of the network. These operations improve the anti-occlusion ability of the network. Second， the horizontal residual is constructedagain in the residual module. The output feature maps contain the same number and different sizes of receptive fields per module. Feature extraction occurs at a more fine-grained level， thereby enriching the multi-scale representation ability of the network. The experimental results show that the improved network provides 2.1% mAP performance on the Pascal visual object classes （VOC） vehicle telemetry dataset and a 1.7% mAP performance on the MS COCO vehicle telemetry dataset. The performance of the improved network is more powerful and its anti-occlusion ability is enhanced. Compared with the original network， the detection results are more competitive.