Volume 24 Issue 5
Oct.  2024
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2024  >  24(5): 12-22
TENG Jing, LI Long-kai, YANG Qi, SHI Rui-feng. Desirable energy space identification of clean and self-consistent energy along railways[J]. Journal of Traffic and Transportation Engineering, 2024, 24(5): 12-22. doi: 10.19818/j.cnki.1671-1637.2024.05.002
Citation: TENG Jing, LI Long-kai, YANG Qi, SHI Rui-feng. Desirable energy space identification of clean and self-consistent energy along railways[J]. Journal of Traffic and Transportation Engineering, 2024, 24(5): 12-22. doi: 10.19818/j.cnki.1671-1637.2024.05.002
shu

Desirable energy space identification of clean and self-consistent energy along railways

doi: 10.19818/j.cnki.1671-1637.2024.05.002

  • School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China

Funds:

National Key Research and Development Program of China 2021YFB2601300

Fundamental Research Funds for the Central Universities 2023JC007

More Information
  • Author Bio:

    TENG Jing(1981-), female, associate professor, PhD, jing.teng@ncepu.edu.cn

    SHI Rui-feng(1977-), male, professor, PhD, shi.ruifeng@ncepu.edu.cn

  • Received Date: 2024-04-09
    Available Online: 2024-12-20
  • Publish Date: 2024-10-25
    Abstract
  • In order to intelligently identify the desirable energy space of clean and self-consistent energy along railways, a remote sensing image dataset containing 210 railway images with a resolution of 4 800 pixel×2 986 pixel was constructed. To address the problem of incompatible channel information of multi-scale fusion units in remote sensing image processing, a self-attention mechanism module was proposed to enhance the ability to capture multi-scale features. To address the discrepancy in the prediction results of remote sensing images with high resolution at different scales, a multi-scale consistency regularization (MSCR) method was proposed to enhance the robustness of the model during image processing. The MSCR-HRNetV2 method was constructed by combining the self-attention mechanism module and the MSCR method to improve the classical image segmentation HRNetV2 method. The MSCR-HRNetV2 method was verified on the self-built remote sensing image dataset of railways and the publicly available Potsdam remote sensing image dataset, respectively. Analysis results show that on the remote sensing image dataset of railways, the improved MSCR-HRNetV2 method achieves a mean intersection over union (MIoU) of 81.37%, which is an improvement of 3.13% compared with the original HRNetV2 method and an improvement of 3.86% compared with the mainstream image segmentation method DeepLabV3+. On the Potsdam remote sensing image dataset, the MIoU of the MSCR-HRNetV2 method reaches 75.96%, which is improved by 2.01% compared to HRNetV2 and 2.19% compared to DeepLabV3+. It can be seen that the improved MSCR-HRNetV2 method significantly improves the semantic segmentation performance of remote sensing images with high resolution, thus intelligently identifying the desirable energy space for clean and self-consistent energy along railways and providing important technical support for the planning and design of the integration system of transportation and energy.

     

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