Traffic object detection models have massive parameters, low detection accuracy and speed, and poor generalization. In view of these problems, YOLOv5 real-time traffic object detection model based on GhostNet and attention mechanism is proposed. The K-means clustering method based on genetic algorithms is used to obtain the best prior bounding box suitable for vehicle detection. The lightweight GhostConv is used to extract target features, and the C3Ghost module based on the CSP structure is constructed, which can greatly reduce the number of model parameters, reduce the calculation cost, and improve the calculation speed. Transformer block and CBAM attention module are added in the feature fusion layer to explore the potential of feature extraction of the model and find attention regions for the model in scenarios with dense objects. The results of ablation experiments and comprehensive performance evaluation on the UA-DETRAC data set show that the average accuracy of the proposed model reaches 98.68%, the number of parameters is 47 M, and the detection speed is 65 FPS. Compared with YOLOv5, the number of parameters is reduced by 34%, the speed is increased by 43%, and the average accuracy is increased by 1.05%.