Optoelectronics Letters, Volume. 19, Issue 3, 179(2023)

Semantic segmentation of urban street scene images based on improved U-Net network

Fuzhen ZHU1、*, Jingyi CUI1, Bing ZHU2, Huiling LI1, and Yan and LIU1
Author Affiliations
  • 1College of Electronic Engineering, Heilongjiang University, Harbin 150080, China
  • 2Institute of Image Information Technology and Engineering, Harbin Institute of Technology, Harbin 150001, China
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    To balance the speed and accuracy in semantic segmentation of the urban street images for autonomous driving, we proposed an improved U-Net network. Firstly, to improve the model representation capability, our improved U-Net network structure was designed as three parts, shallow layer, intermediate layer and deep layer. Different attention mechanisms were used according to their feature extraction characteristics. Specifically, a spatial attention module was used in the shallow network, a dual attention module was used in the intermediate layer network and a channel attention module was used in the deep network. At the same time, the traditional convolution was replaced by depthwise separable convolution in above three parts, which can largely reduce the number of network parameters, and improve the network operation speed greatly. The experimental results on three datasets show that our improved U-Net semantic segmentation model for street images can get better results in both segmentation accuracy and speed. The average mean intersection over union (MIoU) is 68.8%, which is increased by 9.2% and the computation speed is about 38 ms/frame. We can process 27 frames images for segmentation per second, which meets the real-time process and accuracy requirements for semantic segmentation of urban street images.

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    ZHU Fuzhen, CUI Jingyi, ZHU Bing, LI Huiling, and LIU Yan. Semantic segmentation of urban street scene images based on improved U-Net network[J]. Optoelectronics Letters, 2023, 19(3): 179

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    Paper Information

    Received: Jul. 19, 2022

    Accepted: Nov. 18, 2022

    Published Online: Mar. 18, 2023

    The Author Email: Fuzhen ZHU (zhufuzhen@hlju.edu.cn)

    DOI:10.1007/s11801-023-2128-8

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