Ashour, KarimNafaa, SelviaEmad, DoaaMohamed, RanaEssam, HafsaElhenawy, MohammedAshqar, HuthaifaHassan, Abdallah A.Glaser, SebastienRakotonirainy, Andry2024-10-282024-10-282023-09Ashour, Karim, Selvia Nafaa, Doaa Emad, Rana Mohamed, Hafsa Essam, Mohammed Elhenawy, Huthaifa I. Ashqar, Abdallah A. Hassan, Sebastien Glaser, and Andry Rakotonirainy. “Road Sign Classification Using Deep Learning,” in Australasian Road Safety Conference, 2023. September 2023. https://trid.trb.org/View/2431347.https://doi.org/10.33492/ARSC-2023http://hdl.handle.net/11603/368152023 Australasian Road Safety Conference, 19-21 September, Cairns, Queensland, AustraliaRoad sign classification is essential for safety, especially with the development of autonomous vehicles and automated road asset management. Road sign classification is challenging because of several factors, including lighting, weather conditions, motion blur and car vibration. In this study, we developed an ensemble of fine-tuned pre-trained CCN networks. We used the German Traffic Sign Recognition Benchmark (GTSRB) to train and test the proposed ensemble. The proposed ensemble yielded a preliminary testing accuracy of 96.8%. Consequently, we customized the architecture of the worst-performing network in the ensemble, which boosted the accuracy to 99%.2 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.Text