An automated system for traffic sign recognition using convolutional neural network

• Sanam Narejo ^[1] ; Shahnawaz Talpur ^[1] ; Madeha Memon ^[1] ; Amna Rahoo ^[1]
  1. [1] Mehran University of Engineering & Technology (PAK)
• Localización: 3c Tecnología: glosas de innovación aplicadas a la pyme, ISSN-e 2254-4143, Vol. 9, Nº. Extra 1, 2020 (Ejemplar dedicado a: «6th International Conference on Green Computing and Engineering Technologies»), págs. 119-135
• Idioma: inglés
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• Resumen

  • TSR (Traffic Sign Recognition) represents an important feature of advanced driver assistance system, contributing to the safety of the drivers, autonomous vehicles as well and to increase driving comfort. In today’s world road conditions drastically improved as compared with past decades. Obviously, vehicle’s speed increased. So, on driver’s point of view there might be chances of neglecting mandatory road signs while driving. This paper explores the system to helps the driver about recognition of road signs to avoid road accidents. TSR is challenging task, while its accuracy depends on two aspects: feature extractor and classifier. Current popular algorithms mainly deploy CNN (Convolutional Neural Network) to execute both feature extraction and classification. In this paper, we implement the traffic sign recognition by using CNN, the CNN will be trained by using the dataset of 43 different classes of traffic signs along with TensorFlow library. The results will show the 95% accuracy.