{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T15:34:50Z","timestamp":1772552090955,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003448","name":"European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH\u2013CREATE\u2013INNOVATE","doi-asserted-by":"publisher","award":["T1EDK-02374"],"award-info":[{"award-number":["T1EDK-02374"]}],"id":[{"id":"10.13039\/501100003448","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Automated deep learning and data mining algorithms can provide accurate detection, frequency patterns, and predictions of dangerous goods passing through motorways and tunnels. This paper presents a post-processing image detection application and a three-stage deep learning detection algorithm that identifies and records dangerous goods\u2019 passage through motorways and tunnels. This tool receives low-resolution input from toll camera images and offers timely information on vehicles carrying dangerous goods. According to the authors\u2019 experimentation, the mean accuracy achieved by stage 2 of the proposed algorithm in identifying the ADR plates is close to 96% and 92% of both stages 1 and 2 of the algorithm. In addition, for the successful optical character recognition of the ADR numbers, the algorithm\u2019s stage 3 mean accuracy is between 90 and 97%, and overall successful detection and Optical Character Recognition accuracy are close to 94%. Regarding execution time, the proposed algorithm can achieve real-time detection capabilities by processing one image in less than 2.69 s.<\/jats:p>","DOI":"10.3390\/a15100370","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T21:07:11Z","timestamp":1665436031000},"page":"370","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep Learning Process and Application for the Detection of Dangerous Goods Passing through Motorway Tunnels"],"prefix":"10.3390","volume":"15","author":[{"given":"George","family":"Sisias","sequence":"first","affiliation":[{"name":"Department of Informatics, University of Western Macedonia, 52100 Kastoria, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8736-017X","authenticated-orcid":false,"given":"Myrto","family":"Konstantinidou","sequence":"additional","affiliation":[{"name":"Systems Reliability and Industrial Safety Laboratory, Institute for Nuclear and Radiological Sciences, Energy, Technology and Safety, NCSR Demokritos, Ag. Paraskevi, 15341 Athens, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1360-3367","authenticated-orcid":false,"given":"Sotirios","family":"Kontogiannis","sequence":"additional","affiliation":[{"name":"Laboratory Team of Distributed Microcomputer Systems, Department of Mathematics, University of Ioannina, 45110 Ioannina, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.tust.2016.12.002","article-title":"Exploring driving habits and safety critical behavioural intentions among road tunnel users: A questionnaire survey in Greece","volume":"63","author":"Kirytopoulos","year":"2017","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.tust.2009.07.006","article-title":"Technical note","volume":"25","author":"Beard","year":"2010","journal-title":"Tunn. Undergr. 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