{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T13:50:21Z","timestamp":1770472221628,"version":"3.49.0"},"reference-count":21,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T00:00:00Z","timestamp":1741046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Molise CTE Project","award":["#D33B22000060001"],"award-info":[{"award-number":["#D33B22000060001"]}]},{"name":"MIMIT (FSC 2014\u20132020)","award":["#D33B22000060001"],"award-info":[{"award-number":["#D33B22000060001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Public transportation plays a crucial role in our lives, and the road network is a vital component in the implementation of smart cities. Recent advancements in AI have enabled the development of advanced monitoring systems capable of detecting anomalies in road surfaces and road signs, which can lead to serious accidents. This paper presents an innovative approach to enhance road safety through the detection and classification of traffic signs and road surface damage using advanced deep learning techniques (CNN), achieving over 90% precision and accuracy in both detection and classification of traffic signs and road surface damage. This integrated approach supports proactive maintenance strategies, improving road safety and resource allocation for the Molise region and the city of Campobasso. The resulting system, developed as part of the CTE Molise research project funded by the Italian Minister of Economic Growth (MIMIT), leverages cutting-edge technologies such as cloud computing and High-Performance Computing with GPU utilization. It serves as a valuable tool for municipalities, for the quick detection of anomalies and the prompt organization of maintenance operations.<\/jats:p>","DOI":"10.3390\/computers14030091","type":"journal-article","created":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T09:01:33Z","timestamp":1741078893000},"page":"91","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improving Road Safety with AI: Automated Detection of Signs and Surface Damage"],"prefix":"10.3390","volume":"14","author":[{"given":"Davide","family":"Merolla","sequence":"first","affiliation":[{"name":"Dipartimento di Bioscienze e Territorio, Universit\u00e0 degli Studi del Molise, 86090 Pesche, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4644-848X","authenticated-orcid":false,"given":"Vittorio","family":"Latorre","sequence":"additional","affiliation":[{"name":"Dipartimento di Bioscienze e Territorio, Universit\u00e0 degli Studi del Molise, 86090 Pesche, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4012-7490","authenticated-orcid":false,"given":"Antonio","family":"Salis","sequence":"additional","affiliation":[{"name":"Research & Innovation Division, Tiscali Italia S.p.A., 09122 Cagliari, Italy"}]},{"given":"Gianluca","family":"Boanelli","sequence":"additional","affiliation":[{"name":"Research & Innovation Division, Tiscali Italia S.p.A., 09122 Cagliari, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2024, December 15). Road Safety. Available online: https:\/\/www.who.int\/health-topics\/road-safety."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1109\/TITS.2007.895311","article-title":"Road-sign detection and recognition based on support vector machines","volume":"8","year":"2007","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1109\/TVT.2003.810999","article-title":"Road-sign detection and tracking","volume":"52","author":"Fang","year":"2003","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yang, W., and Zhang, W. (2020, January 29\u201330). Real-time traffic signs detection based on YOLO network model. Proceedings of the IEEE 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), Chongqing, China.","DOI":"10.1109\/CyberC49757.2020.00066"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, X. (2023, January 29\u201331). Traffic sign detection based on YOLO v3. Proceedings of the 2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA), Shenyang, China.","DOI":"10.1109\/ICPECA56706.2023.10075795"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"113941","DOI":"10.1109\/ACCESS.2023.3323618","article-title":"Sign-YOLO: A novel lightweight detection model for Chinese traffic sign","volume":"11","author":"Song","year":"2023","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"966","DOI":"10.5755\/j01.itc.52.4.34039","article-title":"Traffic sign detection algorithm based on improved YOLOX","volume":"52","author":"Xu","year":"2023","journal-title":"Inf. Technol. Control"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ciresan, D.C., Meier, U., Masci, J., and Schmidhuber, J. (2012, January 16\u201321). Multi-column deep neural networks for traffic sign classification. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Song, W., and Suandi, S.A. (2023). TSR-YOLO: A Chinese traffic sign recognition algorithm for intelligent vehicles in complex scenes. Sensors, 23.","DOI":"10.3390\/s23020749"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3953","DOI":"10.1007\/s12205-021-1796-9","article-title":"Model for the identification and classification of partially damaged and vandalized traffic signs","volume":"25","author":"Trpkovic","year":"2021","journal-title":"KSCE J. Civ. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Acilo, N., Cruz, A.G.S.D., Kaw, M.K.L., Mabanta, M.D., Pineda, V.G.G., and Roxas, E.A. (2018, January 9\u201310). Traffic sign integrity analysis using deep learning. Proceedings of the 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA), Penang, Malaysia.","DOI":"10.1109\/CSPA.2018.8368695"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"21465","DOI":"10.1007\/s00521-021-05982-z","article-title":"Synthetic data generation using DCGAN for improved traffic sign recognition","volume":"34","author":"Dewi","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1111\/mice.12297","article-title":"Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network","volume":"32","author":"Zhang","year":"2017","journal-title":"Comput. -Aided Civ. Infrastruct. Eng."},{"key":"ref_14","unstructured":"Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., and Omata, H. (2018, January 10\u201313). Road damage detection using deep neural networks with images captured through a smartphone. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Strutu, M., Stamatescu, G., and Popescu, D. (2013, January 11\u201313). A mobile sensor network based road surface monitoring system. Proceedings of the 2013 17th International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania.","DOI":"10.1109\/ICSTCC.2013.6689030"},{"key":"ref_16","first-page":"64","article-title":"Distributed road surface condition monitoring using mobile phones","volume":"3","author":"Perttunen","year":"2011","journal-title":"J. Ambient Intell. Smart Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tarun, R., and Esther, B.P. (2023, January 5\u20136). Real-time regional road sign detection and identification using Raspberry Pi. Proceedings of the 2023 International Conference on Networking and Communications (ICNWC), Chennai, India.","DOI":"10.1109\/ICNWC57852.2023.10127370"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lim, X.R., Lee, C.P., Lim, K.M., Ong, T.S., Alqahtani, A., and Ali, M. (2023). Recent advances in traffic sign recognition: Approaches and datasets. Sensors, 23.","DOI":"10.3390\/s23104674"},{"key":"ref_19","unstructured":"Pensa, D. (2017). Integration of GPS Data into Predictive Models for Tyre Maintenance. [Master\u2019s Thesis, Politecnico di Milano]. Available online: https:\/\/www.politesi.polimi.it\/retrieve\/a81cb05c-7f41-616b-e053-1605fe0a889a\/tesi.pdf."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Neuhold, G., Ollmann, T., Rota Bul\u00f2, S., and Kontschieder, P. (2017, January 22\u201329). The Mapillary Vistas Dataset for semantic understanding of street scenes. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.534"},{"key":"ref_21","unstructured":"Arya, D., Maeda, H., and Ghosh, S.K. (2022). RDD2022: A multi-national image dataset for automatic road damage detection. Geosci. Data J."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/3\/91\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:47:03Z","timestamp":1760028423000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/3\/91"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,4]]},"references-count":21,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["computers14030091"],"URL":"https:\/\/doi.org\/10.3390\/computers14030091","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,4]]}}}