187 / 2023-08-31 22:44:56
Tunnel information understanding and recognition based on multi-scale, multi-channel and multi-granularity feature information fusion
multi-scale,multi-channel,multi-granularity,feature fusion,tunnel information understanding,tunnel information recognition
Abstract Pending
Zhenxin Zhang / Capital Normal University;College of Resources, Environment and Tourism
Haili Sun / College of Resources, Environment and Tourism, Capital Normal University, Beijing 100048, China
Tunnel information understanding and recognition is an important research field that involves accurate extraction and identification of key information in tunnel scenes. To improve the accuracy of tunnel information understanding and recognition, we have designed the methods of multi-scale, multi-channel, and multi-granularity feature fusion. Multi-scale feature fusion considers observed information at different scales to capture both details and overall characteristics of tunnel scenes, enhancing the representation capability of information. Multi-channel feature fusion integrates information from different sensors or data sources to provide a more comprehensive understanding and recognition of various tunnel features. Meanwhile, multi-granularity feature fusion focuses on feature extraction and combination at different granularity levels to obtain a more holistic representation of tunnel information. Through these fusion methods, the precision and robustness of tunnel information understanding and recognition can be effectively improved, providing strong support for tunnel construction, maintenance, and safety management, among other applications.
Important Date
  • Conference Date

    Oct 26

    2023

    to

    Oct 29

    2023

  • Oct 15 2023

    Abstract Submission Deadline

  • Oct 15 2023

    Draft paper submission deadline

  • Nov 13 2023

    Registration deadline

Sponsored By
International Society for Mine Surveying
China Coal Society
China Surveying and Mapping Society
Organized By
中国矿业大学
中国煤炭科工集团有限公司