{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T05:28:43Z","timestamp":1736227723864,"version":"3.32.0"},"reference-count":50,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T00:00:00Z","timestamp":1728259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2022YFF0711704"],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Xinjiang Transportation Industry Science and Technology Project","award":["2022-ZD-006"]},{"name":"Xinjiang R&D Project","award":["ZKXFWCG2022060004"]},{"name":"Xinjiang Transportation Design Institute","award":["KY2022041101"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"The Tianshan Expressway plays a crucial role in China\u2019s \u201cBelt and Road\u201d strategy, yet the extreme climate of the Tianshan Mountains poses significant traffic safety risks, hindering local economic development. Efficient detection of hazardous road surface conditions (RSCs) is vital to address these challenges. The complexity and variability of RSCs in the region, exacerbated by harsh weather, make traditional surveillance methods inadequate for real-time monitoring. To overcome these limitations, a vision-based artificial intelligence approach is urgently needed to ensure effective, real-time detection of dangerous RSCs in the Tianshan road network. This paper analyzes the primary structures and architectures of mainstream neural networks and explores their performance for RSC recognition through a comprehensive set of experiments, filling a research gap. Additionally, T-Net, specifically designed for the Tianshan Expressway engineering project, is built upon the optimal architecture identified in this study. Leveraging the split-transform-merge structure paradigm and asymmetric convolution, the model excels in capturing detailed information by learning features across multiple dimensions and perspectives. Furthermore, the integration of channel, spatial, and multi-head attention modules enhances the weighting of key features, making the T-Net particularly effective in recognizing the characteristics of snow-covered and icy road surfaces. All models presented in this paper were trained on a custom RSC dataset, compiled from various sources. Experimental results indicate that the T-Net outperforms fourteen once state-of-the-art (SOTA) models and three models specifically designed for RSC recognition, with 97.44% accuracy and 9.79% loss on the validation set.<\/jats:p>","DOI":"10.3390\/rs16193727","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T14:58:32Z","timestamp":1728313112000},"page":"3727","source":"Crossref","is-referenced-by-count":1,"title":["Automated Recognition of Snow-Covered and Icy Road Surfaces Based on T-Net of Mount Tianshan"],"prefix":"10.3390","volume":"16","author":[{"given":"Jingqi","family":"Liu","sequence":"first","affiliation":[{"name":"National Cryosphere Desert Data Center, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730030, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8905-9006","authenticated-orcid":false,"given":"Yaonan","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Cryosphere Desert Data Center, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730030, China"}]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[{"name":"Xinjiang Transportation Planning Survey and Design Institute, Urumchi 830094, China"},{"name":"Xinjiang Key Laboratory for Safety and Health of Transportation Infrastructure in Alpine and High-Altitude Mountainous Areas, Urumchi 830094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7059-9907","authenticated-orcid":false,"given":"Zhaobin","family":"Wang","sequence":"additional","affiliation":[{"name":"National Cryosphere Desert Data Center, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730030, China"},{"name":"School of Information Science and Engineering, Lanzhou University, Lanzhou 730030, China"}]},{"given":"Zhixing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,7]]},"reference":[{"key":"ref_1","first-page":"191","article-title":"Research progress of road icing monitoring technology","volume":"4","author":"Ou","year":"2013","journal-title":"Highway"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1784","DOI":"10.1175\/1520-0450-39.10.1784","article-title":"Fuzzy categorization of weather conditions for thermal mapping","volume":"39","author":"Shao","year":"2000","journal-title":"J. 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