Urban road environment perception for automated driving based on an improved YOLOP

Hanzhong Zhang; Zhen Chen

doi:10.1117/12.2656607

20 October 2022 Urban road environment perception for automated driving based on an improved YOLOP

Hanzhong Zhang, Zhen Chen

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 1245115 (2022) https://doi.org/10.1117/12.2656607
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

Vehicle target detection and road scene segmentation in real time are essential tasks in the automatic driving system. This paper proposed an end-to-end inference model by improving the YOLOP. The improved inception structure was added as the backbone. CBAM module was used to reduce the loss of space caused by pooling. To obtain the richer feature information, the fusion of dilated convolution and standard convolution module was applied, and mixed mode of group convolution and depth separable convolution was used in different structures to improve the speed of the model and reduce the parameters. The experimental results show the accuracy of detection is slightly improved. Furthermore, the speed of improved model can reach to 54.8FPS, up by 33.1% from the original YOLOP model.

Citation Download Citation

Hanzhong Zhang and Zhen Chen "Urban road environment perception for automated driving based on an improved YOLOP", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 1245115 (20 October 2022); https://doi.org/10.1117/12.2656607

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