{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T14:37:16Z","timestamp":1784299036722,"version":"3.55.0"},"reference-count":49,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T00:00:00Z","timestamp":1683158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","award":["HV22C0233"],"award-info":[{"award-number":["HV22C0233"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","award":["NRF2022R1G1A1010226"],"award-info":[{"award-number":["NRF2022R1G1A1010226"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"Ministry of Science and ICT (MSIT), South Korea","doi-asserted-by":"publisher","award":["HV22C0233"],"award-info":[{"award-number":["HV22C0233"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"Ministry of Science and ICT (MSIT), South Korea","doi-asserted-by":"publisher","award":["NRF2022R1G1A1010226"],"award-info":[{"award-number":["NRF2022R1G1A1010226"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Hundreds of people are injured or killed in road accidents. These accidents are caused by several intrinsic and extrinsic factors, including the attentiveness of the driver towards the road and its associated features. These features include approaching vehicles, pedestrians, and static fixtures, such as road lanes and traffic signs. If a driver is made aware of these features in a timely manner, a huge chunk of these accidents can be avoided. This study proposes a computer vision-based solution for detecting and recognizing traffic types and signs to help drivers pave the door for self-driving cars. A real-world roadside dataset was collected under varying lighting and road conditions, and individual frames were annotated. Two deep learning models, YOLOv7 and Faster RCNN, were trained on this custom-collected dataset to detect the aforementioned road features. The models produced mean Average Precision (mAP) scores of 87.20% and 75.64%, respectively, along with class accuracies of over 98.80%; all of these were state-of-the-art. The proposed model provides an excellent benchmark to build on to help improve traffic situations and enable future technological advances, such as Advance Driver Assistance System (ADAS) and self-driving cars.<\/jats:p>","DOI":"10.3390\/s23094466","type":"journal-article","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T02:56:51Z","timestamp":1683255411000},"page":"4466","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Road Feature Detection for Advance Driver Assistance System Using Deep Learning"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6214-2678","authenticated-orcid":false,"given":"Hamza","family":"Nadeem","sequence":"first","affiliation":[{"name":"Engineering and Management Sciences, Balochistan University of Information Technology Engineering & Management Sciences, Quetta 87300, Pakistan"},{"name":"School of Mechanical and Manufacturing Engineering, National University of Science and Technology, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kashif","family":"Javed","sequence":"additional","affiliation":[{"name":"School of Mechanical and Manufacturing Engineering, National University of Science and Technology, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0608-0828","authenticated-orcid":false,"given":"Zain","family":"Nadeem","sequence":"additional","affiliation":[{"name":"Engineering and Management Sciences, Balochistan University of Information Technology Engineering & Management Sciences, Quetta 87300, Pakistan"},{"name":"School of Mechanical and Manufacturing Engineering, National University of Science and Technology, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Jawad","family":"Khan","sequence":"additional","affiliation":[{"name":"School of Mechanical and Manufacturing Engineering, National University of Science and Technology, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3208-5275","authenticated-orcid":false,"given":"Saddaf","family":"Rubab","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, Sharjah 27272, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1628-9948","authenticated-orcid":false,"given":"Dong Keon","family":"Yon","sequence":"additional","affiliation":[{"name":"Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul 02447, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7473-8441","authenticated-orcid":false,"given":"Rizwan Ali","family":"Naqvi","sequence":"additional","affiliation":[{"name":"Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,4]]},"reference":[{"key":"ref_1","unstructured":"(2022, July 29). 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