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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">JERA</journal-id><journal-title-group><journal-title>Journal of Electronic Research and Application</journal-title></journal-title-group><issn>2208-3502</issn><eissn>2208-3510</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/jera.v10i1.13982</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Intelligent Identification of Water Accumulation and Ice Formation in Traffic Tunnels</title><url>https://artdesignp.com/journal/JERA/10/1/10.26689/jera.v10i1.13982</url><author>ChenBeining</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>10</volume><issue>1</issue><history><date date-type="pub"><published-time>2026-02-27</published-time></date></history><abstract>Water accumulation and ice formation in traffic tunnels pose prominent safety hazards (e.g., reduced road friction, increased traffic accidents) and threaten structural integrity (e.g., damage to waterproof layers and lining structures). Therefore, the intelligent identification of these two hazards is crucial for safeguarding traffic safety and optimizing tunnel maintenance strategies. The intelligent identification system integrates computer vision, deep learning, and multi-source sensor data fusion technologies. Current state-of-the-art practices adopt deep learning models for target segmentation and detection, combined with robust image preprocessing and post-processing techniques. This technology exhibits significant practical application value, and its continuous innovation and development are expected to substantially enhance the level of tunnel safety management and structural durability preservation.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Li X, Xiong Z, Li X, 2024, Research on Intelligent Detection of Apparent Defects in Old Tunnels Based on Semantic Segmentation. 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