To realize the real-time detection of vehicle targets in an edge computing environment, we improved the YOLOv5Nano (YOLOv5n) model to develop a lightweight, high-precision, and real-time detection model called slimming, CBAM, distillation, YOLO (SCD-YOLO). By introducing the convolutional block attention mechanism, we aimed to increase the attention devoted to channel and spatial feature information, thereby improving feature extraction capabilities. The adoption of the slimming pruning algorithm further improved the weight and computational efficiency of the model. Finally, in the fine-tuning stage of the model, knowledge distillation technology was applied to use a model with a large number of parameters and high accuracy as a teacher model to guide the pruned model to compensate for a loss of accuracy. Experimental results demonstrate that compared with the original YOLOv5n model, on the University at Albany Detection and Tracking vehicle dataset, SCD-YOLO reduced the parameter count by 44.4% (approximately 4M parameters) and the calculation count by 40.4% while increasing processing speed by 14.7% with an accuracy loss of only 0.5%, which meets the requirements of real-time vehicle target detection in an edge computing environment.