{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T15:39:57Z","timestamp":1781710797487,"version":"3.54.5"},"reference-count":45,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T00:00:00Z","timestamp":1683763200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003093","name":"Ministry of Higher Education","doi-asserted-by":"publisher","award":["FRGS\/1\/2021\/ICT02\/MMU\/02\/4"],"award-info":[{"award-number":["FRGS\/1\/2021\/ICT02\/MMU\/02\/4"]}],"id":[{"id":"10.13039\/501100003093","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003093","name":"Ministry of Higher Education","doi-asserted-by":"publisher","award":["RGP2\/332\/44"],"award-info":[{"award-number":["RGP2\/332\/44"]}],"id":[{"id":"10.13039\/501100003093","id-type":"DOI","asserted-by":"publisher"}]},{"name":"King Khalid University","award":["FRGS\/1\/2021\/ICT02\/MMU\/02\/4"],"award-info":[{"award-number":["FRGS\/1\/2021\/ICT02\/MMU\/02\/4"]}]},{"name":"King Khalid University","award":["RGP2\/332\/44"],"award-info":[{"award-number":["RGP2\/332\/44"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Autonomous vehicles have become a topic of interest in recent times due to the rapid advancement of automobile and computer vision technology. The ability of autonomous vehicles to drive safely and efficiently relies heavily on their ability to accurately recognize traffic signs. This makes traffic sign recognition a critical component of autonomous driving systems. To address this challenge, researchers have been exploring various approaches to traffic sign recognition, including machine learning and deep learning. Despite these efforts, the variability of traffic signs across different geographical regions, complex background scenes, and changes in illumination still poses significant challenges to the development of reliable traffic sign recognition systems. This paper provides a comprehensive overview of the latest advancements in the field of traffic sign recognition, covering various key areas, including preprocessing techniques, feature extraction methods, classification techniques, datasets, and performance evaluation. The paper also delves into the commonly used traffic sign recognition datasets and their associated challenges. Additionally, this paper sheds light on the limitations and future research prospects of traffic sign recognition.<\/jats:p>","DOI":"10.3390\/s23104674","type":"journal-article","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T01:30:29Z","timestamp":1683855029000},"page":"4674","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["Recent Advances in Traffic Sign Recognition: Approaches and Datasets"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8391-8457","authenticated-orcid":false,"given":"Xin Roy","family":"Lim","sequence":"first","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3679-8977","authenticated-orcid":false,"given":"Chin Poo","family":"Lee","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1929-7978","authenticated-orcid":false,"given":"Kian Ming","family":"Lim","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5867-9517","authenticated-orcid":false,"given":"Thian Song","family":"Ong","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1052-2657","authenticated-orcid":false,"given":"Ali","family":"Alqahtani","sequence":"additional","affiliation":[{"name":"Department of Computer Science, King Khalid University, Abha 61421, Saudi Arabia"},{"name":"Center for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5908-4013","authenticated-orcid":false,"given":"Mohammed","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Computer Science, King Khalid University, Abha 61421, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kerim, A., and Efe, M.\u00d6. (2021, January 13\u201316). Recognition of Traffic Signs with Artificial Neural Networks: A Novel Dataset and Algorithm. Proceedings of the 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Jeju Island, Republic of Korea.","DOI":"10.1109\/ICAIIC51459.2021.9415238"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Soni, D., Chaurasiya, R.K., and Agrawal, S. (2019, January 20\u201322). Improving the Classification Accuracy of Accurate Traffic Sign Detection and Recognition System Using HOG and LBP Features and PCA-Based Dimension Reduction. Proceedings of the International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Amity University Rajasthan, Jaipur, India.","DOI":"10.2139\/ssrn.3358756"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Namyang, N., and Phimoltares, S. (2020, January 21\u201322). Thai traffic sign classification and recognition system based on histogram of gradients, color layout descriptor, and normalized correlation coefficient. Proceedings of the 2020-5th International Conference on Information Technology (InCIT), Chonburi, Thailand.","DOI":"10.1109\/InCIT50588.2020.9310778"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"159","DOI":"10.46300\/9106.2022.16.20","article-title":"Finely Crafted Features for Traffic Sign Recognition","volume":"16","author":"Li","year":"2022","journal-title":"Int. J. Circuits Syst. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2807","DOI":"10.1007\/s00521-017-2887-x","article-title":"Traffic sign recognition based on color, shape, and pictogram classification using support vector machines","volume":"30","author":"Madani","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sapijaszko, G., Alobaidi, T., and Mikhael, W.B. (2019, January 4\u20137). Traffic sign recognition based on multilayer perceptron using DWT and DCT. Proceedings of the 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS), Dallas, TX, USA.","DOI":"10.1109\/MWSCAS.2019.8884897"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.procs.2018.01.109","article-title":"Traffic sign recognition based on multi-feature fusion and ELM classifier","volume":"127","author":"Aziz","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Weng, H.M., and Chiu, C.T. (2018, January 15\u201320). Resource efficient hardware implementation for real-time traffic sign recognition. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8462298"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"03014","DOI":"10.1051\/shsconf\/202214403014","article-title":"Research on the Optimal Machine Learning Classifier for Traffic Signs","volume":"Volume 144","author":"Wang","year":"2022","journal-title":"SHS Web of Conferences"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Siniosoglou, I., Sarigiannidis, P., Spyridis, Y., Khadka, A., Efstathopoulos, G., and Lagkas, T. (2021, January 14\u201316). Synthetic Traffic Signs Dataset for Traffic Sign Detection & Recognition In Distributed Smart Systems. Proceedings of the 2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS), Pafos, Cyprys.","DOI":"10.1109\/DCOSS52077.2021.00056"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1109\/TITS.2018.2843815","article-title":"Real-time traffic sign recognition based on efficient CNNs in the wild","volume":"20","author":"Li","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"17779","DOI":"10.1007\/s11042-022-12163-0","article-title":"Traffic sign recognition based on deep learning","volume":"81","author":"Zhu","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1016\/j.proeng.2017.09.594","article-title":"CNN design for real-time traffic sign recognition","volume":"201","author":"Shustanov","year":"2017","journal-title":"Procedia Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.procs.2019.12.108","article-title":"Autonomous traffic sign (ATSR) detection and recognition using deep CNN","volume":"163","author":"Alghmgham","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"044034","DOI":"10.1088\/1757-899X\/688\/4\/044034","article-title":"Traffic sign recognition with a small convolutional neural network","volume":"Volume 688","author":"Li","year":"2019","journal-title":"IOP Conference Series: Materials Science And Engineering"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.isprsjprs.2020.10.003","article-title":"Improving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation","volume":"171","author":"Yazdan","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"53330","DOI":"10.1109\/ACCESS.2019.2912311","article-title":"Real-time embedded traffic sign recognition using efficient convolutional neural network","volume":"7","author":"Bangquan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8870529","DOI":"10.1155\/2021\/8870529","article-title":"A lightweight model for traffic sign classification based on enhanced LeNet-5 network","volume":"2021","author":"Zaibi","year":"2021","journal-title":"J. Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1952","DOI":"10.22214\/ijraset.2021.37700","article-title":"Traffic Sign Classification Using CNN","volume":"9","author":"Sreya","year":"2021","journal-title":"Int. J. Res. Appl. Sci. Eng. Technol."},{"key":"ref_20","first-page":"3194","article-title":"Highly sensitive Deep Learning Model for Road Traffic Sign Identification","volume":"71","author":"Abudhagir","year":"2022","journal-title":"Math. Stat. Eng. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mehta, S., Paunwala, C., and Vaidya, B. (2019, January 15\u201317). CNN based traffic sign classification using Adam optimizer. Proceedings of the 2019 International Conference on Intelligent Computing and Control Systems (ICCS), Madurai, India.","DOI":"10.1109\/ICCS45141.2019.9065537"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s12243-019-00731-9","article-title":"Lightweight deep network for traffic sign classification","volume":"75","author":"Zhang","year":"2020","journal-title":"Ann. Telecommun."},{"key":"ref_23","first-page":"165","article-title":"Traffic sign classification comparison between various convolution neural network models","volume":"12","author":"Sokipriala","year":"2021","journal-title":"Int. J. Sci. Eng. Res."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Vincent, M.A., Vidya, K., and Mathew, S.P. (2020, January 3\u20135). Traffic sign classification using deep neural network. Proceedings of the 2020 IEEE Recent Advances in Intelligent Computational Systems (RAICS), Thiruvananthapuram, India.","DOI":"10.1109\/RAICS51191.2020.9332474"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Madan, R., Agrawal, D., Kowshik, S., Maheshwari, H., Agarwal, S., and Chakravarty, D. (2019, January 19\u201321). Traffic Sign Classification using Hybrid HOG-SURF Features and Convolutional Neural Networks. Proceedings of the ICPRAM 2019-8th International Conference on Pattern Recognition Applications and Methods, Prague, Czech Republic.","DOI":"10.5220\/0007392506130620"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"78136","DOI":"10.1109\/ACCESS.2018.2884826","article-title":"Classification of traffic signs: The european dataset","volume":"6","author":"Serna","year":"2018","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"18915","DOI":"10.1007\/s11042-022-12531-w","article-title":"An effective automatic traffic sign classification and recognition deep convolutional networks","volume":"81","author":"Mishra","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhao, G., Zhou, J., and Kuang, L. (2017, January 26\u201329). Real-time traffic sign classification using combined convolutional neural networks. Proceedings of the 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR), Nanjing, China.","DOI":"10.1109\/ACPR.2017.12"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3041117","DOI":"10.1155\/2022\/3041117","article-title":"Evaluation of Vision Transformers for Traffic Sign Classification","volume":"2022","author":"Zheng","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"114481","DOI":"10.1016\/j.eswa.2020.114481","article-title":"DeepThin: A novel lightweight CNN architecture for traffic sign recognition without GPU requirements","volume":"168","author":"Haque","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_31","first-page":"250","article-title":"Traffic Sign Classification Using Deep Learning","volume":"12","author":"Usha","year":"2021","journal-title":"Turk. J. Comput. Math. Educ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3995209","DOI":"10.1155\/2022\/3995209","article-title":"A small network MicronNet-BF of traffic sign classification","volume":"2022","author":"Fang","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sarku, E., Steele, J., Ruffin, T., Gokaraju, B., and Karimodini, A. (2021, January 10\u201313). Reducing Data Costs-Transfer Learning Based Traffic Sign Classification Approach. Proceedings of the SoutheastCon 2021, Atlanta, GA, USA.","DOI":"10.1109\/SoutheastCon45413.2021.9401900"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Cao, J., Song, C., Peng, S., Xiao, F., and Song, S. (2019). Improved traffic sign detection and recognition algorithm for intelligent vehicles. Sensors, 19.","DOI":"10.3390\/s19184021"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Fu, H., and Wang, H. (2021, January 26\u201328). Traffic Sign Classification Based on Prototypes. Proceedings of the 2021 16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Chengdu, China.","DOI":"10.1109\/ISKE54062.2021.9755432"},{"key":"ref_36","first-page":"546","article-title":"Effect of various dimension convolutional layer filters on traffic sign classification accuracy","volume":"19","author":"Sichkar","year":"2019","journal-title":"Sci. Tech. J. Inf. Technol. Mech. Opt."},{"key":"ref_37","unstructured":"Agarwal, S., X, C., and Kumar, R. (2022). Convolutional Neural Network for Traffic Sign Classification. Int. J. Inf. Technol. Proj. Manag., 9."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e11792","DOI":"10.1016\/j.heliyon.2022.e11792","article-title":"Traffic sign classification using CNN and detection using faster-RCNN and YOLOV4","volume":"8","author":"Youssouf","year":"2022","journal-title":"Heliyon"},{"key":"ref_39","first-page":"63","article-title":"Accuracy Comparison of CNN Networks on GTSRB Dataset","volume":"2","author":"Durdu","year":"2022","journal-title":"J. Artif. Intell. Data Sci."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kuros, S., and Kryjak, T. (2022). Traffic Sign Classification Using Deep and Quantum Neural Networks. arXiv.","DOI":"10.36227\/techrxiv.21251895.v1"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Pradana, A.I., Rustad, S., Shidik, G.F., and Santoso, H.A. (2022, January 17\u201318). Indonesian Traffic Signs Recognition Using Convolutional Neural Network. Proceedings of the 2022 International Seminar on Application for Technology of Information and Communication (iSemantic), Semarang, Indonesia.","DOI":"10.1109\/iSemantic55962.2022.9920448"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Bhatt, N., Laldas, P., and Lobo, V.B. (2022, January 22\u201324). A Real-Time Traffic Sign Detection and Recognition System on Hybrid Dataset using CNN. Proceedings of the 2022 7th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India.","DOI":"10.1109\/ICCES54183.2022.9835954"},{"key":"ref_43","first-page":"88","article-title":"Lightweight Residual Layers Based Convolutional Neural Networks for Traffic Sign Recognition","volume":"2","author":"Mamatkulovich","year":"2022","journal-title":"Eur. Int. J. Multidiscip. Res. Manag. Stud."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Stallkamp, J., Schlipsing, M., Salmen, J., and Igel, C. (August, January 31). The German traffic sign recognition benchmark: A multi-class classification competition. Proceedings of the 2011 International Joint Conference on Neural Networks, San Jose, CA, USA.","DOI":"10.1109\/IJCNN.2011.6033395"},{"key":"ref_45","unstructured":"Timofte, R., Prisacariu, V.A., Gool, L.V., and Reid, I. (2012). Emerging Topics in Computer Vision and Its Applications, World Scientific."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4674\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:33:11Z","timestamp":1760124791000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4674"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,11]]},"references-count":45,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23104674"],"URL":"https:\/\/doi.org\/10.3390\/s23104674","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,11]]}}}