Road Sign Identification with Convolutional Neural Network Using TensorFlow

Mohammed Kherarba (1) , Mounir Tahar Abbes (1) , Selma Boumerdassi (2) , Mohammed Meddah (1) , Abdelhak Benhamada (1) , Mohammed Senouci (3)

1 UHBC - Université Hassiba Ben Bouali de Chlef
2 CEDRIC - Centre d'études et de recherche en informatique et communications
3 Université d'Oran 1 Ahmed Ben Bella [Oran]

With the use and continuous development of deep learning methods, the recognition of images and scenes captured from the real environment has also undergone a major transformation in the techniques and parameters used. In most of the methods, we notice that recognition is based on extraction. This paper proposes a classification technique based on convolutional features in the context of Traffic Sign Detection and Recognition (TSDR) which uses an enriched dataset of traffic signs. This solution offers an additional level of assistance to the driver, allowing better safety of passengers, road users, and cars. An experimental evaluation on publicly available scene image datasets with convolutional features presents results with an accuracy of 94.7% of our classification model.

Keywords: Traffic Sign Detection; TSDR; CNN; Tensorflow

Domains: Computer Science [cs]

Mohammed Kherarba, Mounir Tahar Abbes, Selma Boumerdassi, Mohammed Meddah, Abdelhak Benhamada, et al.. Road Sign Identification with Convolutional Neural Network Using TensorFlow. Machine Learning for Networking, Nov 2020, Paris, France. pp.255-264, ⟨10.1007/978-3-030-70866-5_17⟩. ⟨hal-03734210⟩