Abstract
Traffic sign recognition is among the major tasks on driver assistance system. The convolutional neural networks (CNN) play an important role to find a good accuracy of traffic sign recognition in order to limit the dangerous acts of the driver and to respect the road laws. The accuracy of the Detection and Classification determines how powerful of the technique used is. Whereas SSD Multibox (Single Shot MultiBox Detector) is an approach based on convolutional neural networks paradigm, it is adopted in this paper, firstly because we can rely on it for the real-time applications, this approach runs on 59 FPS (frame per second). Secondly, in order to optimize difficulties in multiple layers of DeeperCNN to provide a finer accuracy. Moreover, our experiment on German traffic sign recognition benchmark (GTSRB) demonstrated that the proposed approach could achieve competitive results (83.2% in 140.000 learning steps) using GPU parallel system and Tensorflow.
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Notes
- 1.
Speed limit 20 km/h, Speed limit 30 km/h, Speed limit 50 km/h, Speed limit 60 km/h, Speed limit 70 km/h, Speed limit 80 km/h, End of speed limit 80 km/h, Speed limit 100 km/h, Speed limit 120 km/h, No overtaking, No overtaking by lorries, Junction with minor roads, Main road, Give way, Stop and give way, No entry for vehicles (both directions), No lorries, No entry for vehicles, Other hazard, Curve to the left, Curve to the right, Double curve, first to the left, Bumpy road, Danger of skidding, Road narrows (right side), Roadworks, Traffic lights ahead, Caution for pedestrians, Caution school, Caution for bicyclists, Be careful in winter, Wild animals, End of all prohibitions, Turn right ahead, Turn left ahead, Ahead only, Ahead or right only, Ahead or left only, Keep right, Keep left, Roundabout, End of no-overtaking zone, End of no-overtaking zone for lorrie.
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El Ouadrhiri, A.A., Burian, J., Andaloussi, S.J., El Morabet, R., Ouchetto, O., Sekkaki, A. (2018). Fast-Tracking Application for Traffic Signs Recognition. In: Chmielewski, L., Kozera, R., Orłowski, A., Wojciechowski, K., Bruckstein, A., Petkov, N. (eds) Computer Vision and Graphics. ICCVG 2018. Lecture Notes in Computer Science(), vol 11114. Springer, Cham. https://doi.org/10.1007/978-3-030-00692-1_34
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