Abstract
In order to better solve the problem about lack of real time in road region segmentation algorithm, this paper designs a deep neural network of road region segmentation Spp-U-net with U-net structure as the core. In addition, the use of spatial pyramid pooling (Spp) to replace the conventional pooling makes up for the lack of accuracy of the Spp-U-net network. The accuracy of Spp-U-net network algorithm is improved compared with U-net model. The model has strong real-time performance on the road segmentation problem under the premise of ensuring accuracy.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (NSFC) under grant no. 61433015: 61602040, the National Social Science Foundation of China under grant no. 14ZDB154, the key project of the National Language Committee (ZDI135-31), the support plan for high level teacher team construction in Beijing municipal universities (IDHT20170511), the Science and Technology project of Beijing Educational Committee (KM201711417006) and Premium Funding Project for Academic Human Resources Development in Beijing Union University (BPHR2019CZ05).
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Zhang, Y., Luo, D., Yao, D., Liu, H. (2021). Spp-U-net: Deep Neural Network for Road Region Segmentation. In: Liu, Q., Liu, X., Li, L., Zhou, H., Zhao, HH. (eds) Proceedings of the 9th International Conference on Computer Engineering and Networks . Advances in Intelligent Systems and Computing, vol 1143. Springer, Singapore. https://doi.org/10.1007/978-981-15-3753-0_43
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DOI: https://doi.org/10.1007/978-981-15-3753-0_43
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