Aiming at the problems of low efficiency and poor robustness of multi-object detection and segmentation for traffic scenes in intelligent driving, a fast Multi-object detection and segmentation for traffic scene based on improved Mask R-CNN is proposed. Firstly, in order to effectively reduce network parameters and compress model volume, the lightweight MobileNet is used as the backbone network to improve the ability of transplant algorithms on the subsequent embedded side. Secondly, by optimizing the convolution structure of FPN and backbone network to ensure the complete transfer of feature information between high-level layers, the improved network model for multi-object detection and segmentation in traffic scenes is obtained by adjusting hyperparameters. Comparative experiments are conducted under different traffic scenarios, the improved network can accurately realize the detection and segmentation of multiple objects and the average detection accuracy can reach 85. 2% . Migration experiments are carried out on the ApolloScape and NuScence dataset to improve the network, which show good generalization capabilities. The improved backbone structure and network structure optimization proposed in this paper can adapt to a variety of complex traffic scenarios and complete the fast detection and segmentation of multiple object in traffic scenarios. It provides theoretical basis and technical solutions for intelligent driving.