{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T17:50:31Z","timestamp":1771609831818,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T00:00:00Z","timestamp":1753747200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Digital"],"abstract":"<jats:p>Many traffic-sign detection systems are available to assist drivers with particular conditions such as small and distant signs, multiple signs on the road, objects similar to signs, and other challenging conditions. Real-time object detection is an indispensable aspect of these detection systems, with detection speed and efficiency being critical parameters. In terms of these parameters, to enhance performance in road-sign detection under diverse conditions, we proposed a comprehensive methodology, SSAM_YOLOv5, to handle feature extraction and small-road-sign detection performance. The method was based on a modified version of YOLOv5s. First, we introduced attention modules into the backbone to focus on the region of interest within video frames; secondly, we replaced the activation function with the SwishT_C activation function to enhance feature extraction and achieve a balance between inference, precision, and mean average precision (mAP@50) rates. Compared to the YOLOv5 baseline, the proposed improvements achieved remarkable increases of 1.4% and 1.9% in mAP@50 on the Tiny LISA and GTSDB datasets, respectively, confirming their effectiveness.<\/jats:p>","DOI":"10.3390\/digital5030030","type":"journal-article","created":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T16:16:10Z","timestamp":1753805770000},"page":"30","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["SSAM_YOLOv5: YOLOv5 Enhancement for Real-Time Detection of Small Road Signs"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5486-3897","authenticated-orcid":false,"given":"Fatima","family":"Qanouni","sequence":"first","affiliation":[{"name":"National School of Applied Sciences, Sultan Moulay Slimane University, Khouribga 25000, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2098-6877","authenticated-orcid":false,"given":"Hakim","family":"El Massari","sequence":"additional","affiliation":[{"name":"National School of Applied Sciences, Sultan Moulay Slimane University, Khouribga 25000, Morocco"},{"name":"Higher School of Technology, Cadi Ayyad University, El Kel\u00e2a des Sraghna 43000, Morocco"},{"name":"LAMAI Laboratory, Faculty of Sciences and Techniques, Cadi Ayyad University, Marrakech 40000, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2020-249X","authenticated-orcid":false,"given":"Noreddine","family":"Gherabi","sequence":"additional","affiliation":[{"name":"National School of Applied Sciences, Sultan Moulay Slimane University, Khouribga 25000, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9542-3803","authenticated-orcid":false,"given":"Maria","family":"El-Badaoui","sequence":"additional","affiliation":[{"name":"National School of Applied Sciences, Sultan Moulay Slimane University, Khouribga 25000, Morocco"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.3390\/encyclopedia2040119","article-title":"Road Markings and Signs in Road Safety","volume":"2","author":"Fiolic","year":"2022","journal-title":"Encyclopedia"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1007\/s12555-024-0899-8","article-title":"PARA-CAM: Parallel Processing Architecture for Intelligent Real Time Multi IP Camera System with Deep Learning Models","volume":"23","author":"Seo","year":"2025","journal-title":"Int. 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