In order to improve the accuracy and efficiency of trafic intersection target detection,this paper proposes an improved trafic scene target detection model based on YOLOv5s.An efficient layer aggregation network structure is introduced into the backbone network of YOLOv5s to improve the ability to learn target features.The attention mechanism CBAM of channel attention and spatial attention is introduced to further improve the feature extraction ability of the network model.The a-loU is used as the bounding box regression loss function to improve the bounding box regression accuracy.The experimental results show that compared with the original YOLOv5s model,the improved object detection model proposed in this paper has the detection accuracy increased by 2.4%and the model parameter number and model size reduced by 20.9%and 19.1%respectively.It realizes all kinds of targets of accurate and efficient detection of traffic intersection scenes in different time periods,and ensures the application requirements of real-time detection.