## 4 Conclusions  
With the progress of AI technology, DL networks and optimization strategies for semantic segmentation have also been proposed in various industries. However, this technology has not been applied in urban traffic management. Therefore, based on DL theory, methods for efficient semantic segmentation of images using DCNN have been intensively studied. First, the theoretical basis of CNN is briefly stated, and the real-time semantic segmentation technology of urban scenes based on DCNN is introduced in detail. Then, the atrous convolution algorithm and the multi-scale parallel atrous spatial pyramid model are expounded, and an ESNet of real-time semantic segmentation model for autonomous driving scenarios is proposed. The overall network adopts an encoder-decoder structure. The encoder uses a designed hybrid atrous convolution module to extract dense image features and uses convolution kernels of diverse sizes at various levels of the network to extract multi-scale features. A large number of comparative experiments are designed to verify the effectiveness of the network, and the segmentation performance of the model is evaluated on the urban scene dataset Cityscapes. The proposed ESNet structure achieves a segmentation accuracy of 70.7% for the 19 semantic categories and 87.4% for the seven large grouping categories, which are increased to varying degrees compared with other algorithms, and this structure improves the segmentation accuracy of trucks by 22.9% and the segmentation accuracy of cars by 29.6%, which has obvious advantages. Although the proposed method achieves a balance between segmentation accuracy and forward reasoning speed to a certain extent, there are still many shortcomings that need to be further studied and improved. The proposed efficient real-time semantic segmentation method based on DCNN faces huge challenges. Especially with the development of DL technology, many efficient methods have emerged. For example, the Generative Adversarial Networks can be used to learn more discriminative features, Graph network models are used to perform better relational reasoning on the data, NN architecture search is used to reduce manual operations in the design of the model, and the optimal network structure for fitting the data are adaptively selected. The network structure further reduces the memory consumption, storage overhead, and energy overhead of the model. These can further reduce the memory consumption, storage overhead, and energy costs of the model. Compared with the study of [PERSON] et al. (2019) [39], the designed method covers a wider range of fields, and the degree of method optimization is deeper. Therefore, the research will play a more vital role in future social development. Conceptualization, [PERSON] ([PERSON]) and J.S. ; methodology, [PERSON] ([PERSON]); software, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]), [PERSON], and [PERSON] ([PERSON]); formal analysis, [PERSON] ([PERSON]); investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON]; visualization, [PERSON] ([PERSON]); project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript. This work was supported by the National Innovation Training Program for College Students (No. 202010424004) and Frontier Physics Experiment Facility for Extremely Deep Underground Extremely Low Radiation Background of Key National Technology Infrastructure Construction-Subproject of pre-research project (Total No. 2018-000052-73-01-002125). Not applicable. The data involved in this paper can be obtained by contacting the corresponding author. In addition, this study involves two types of datasets. Please refer to the website for details of the Cityscapes dataset: [[https://www.cityscapes-dataset.com](https://www.cityscapes-dataset.com)]([https://www.cityscapes-dataset.com](https://www.cityscapes-dataset.com)) (accessed on 20 May 2022).