[PERSON] (2021) [10] designed an end-to-end depth-supervised fully convolutional network based on convolutional neural networks (CNN) to solve the problem of automatic segmentation of brain tissue in brain images. For 3D brain images, the data are first cut into 2D image slices. On the basis of supervising the fully convolutional network structure, a deep supervision mechanism is added; that is, the loss value feedback is obtained in advance in the multi-level structure of feature extraction. By introducing a deep supervision structure, the improved model achieves a more accurate segmentation effect in the segmentation task of brain tissue. There are six most commonly used segmentation networks, which are semantic Segmentation Network (SegNet), Efficient Segmentation Network (ENet), Efficient Spatial Pyramid Network (ESPNet), Cross Guidance Network (CGNet), Efficient Residual Factorized Conv Network (ERFNet), and Image Cascade Network (ICNet). In addition, the accuracy of the above semantic segmentation is also high [11, 12, 13]. To sum up, there is still a certain gap between the accuracy of real-time semantic segmentation models and high-precisionmodels. Among them, the most obvious gap is the segmentation of small objects. In urban traffic scenes, small objects such as signs and traffic lights are significant for the understanding of traffic scenes. Therefore, how to ensure segmentation accuracy is still the main challenge facing the current semantic segmentation. Based on the existing deep learning network, this study proposes a more efficient and accurate semantic segmentation network, which can better deal with the problem of semantic segmentation in urban environment. We have carried out experimental tests on a variety of common ground objects in the city and found that the ESNet can better complete the task of semantic segmentation. As the modern urban environment becomes more and more complex, in the process of helping us accurately understand the surrounding environment and identify surrounding objects, mobile terminals on vehicles also rely more on the semantic segmentation ability of deep learning networks. The network structure proposed in ESNet not only considers accuracy and efficiency, but also completes the semantic segmentation of scene elements in the city, which will play an important role in the research of urban environment perception and automatic driving. The content structure of the article is as follows. First, introducing the atrous spatial pyramid model and atrous convolution algorithm, an Efficient Symmetric Network (ESNet) of semantic segmentation model for autonomous driving scenes is constructed. Then, the segmentation performance of the model is evaluated on the urban scene dataset. On account of this, the technical principle of an efficient symmetric real-time semantic segmentation network based on DCNN is first expounded. After that, its application design concept in urban traffic is discussed. In the end, the simulation test is carried out by a computer, which comprehensively evaluates the designed ESNet model. The innovation is to apply DL-based image semantic segmentation technology to the smart transportation system to promote the development of urban traffic management. The research not only provides technical support for the development of semantic segmentation but also contributes to the development of intelligent transportation.