This deep learning model utilizes Tensorflow to recognize traffic signs which could be used to deploy in self driving cars.
APPENDIX
In this case study, we want to classify images of traffic signs using deep Convolutional Neural Networks (CNNs). The dataset consists of 43 different classes of images. Classes are as listed below: 0 = Speed limit (20km/h) 1 = Speed limit (30km/h) 2 = Speed limit (50km/h) 3 = Speed limit (60km/h) 4 = Speed limit (70km/h) 5 = Speed limit (80km/h) 6 = End of speed limit (80km/h) 7 = Speed limit (100km/h) 8 = Speed limit (120km/h) 9 = No passing 10 = No passing for vehicles over 3.5 metric tons 11 = Right-of-way at the next intersection 12 = Priority road 13 = Yield 14 = Stop 15 = No vehicles 16 = Vehicles over 3.5 metric tons prohibited 17 = No entry 18 = General caution 19 = Dangerous curve to the left 20 = Dangerous curve to the right 21 = Double curve 22 = Bumpy road 23 = Slippery road 24 = Road narrows on the right 25 = Road work 26 = Traffic signals 27 = Pedestrians 28 = Children crossing 29 = Bicycles crossing 30 = Beware of ice/snow 31 = Wild animals crossing 32 = End of all speed and passing limits 33 = Turn right ahead 34 = Turn left ahead 35 = Ahead only 36 = Go straight or right 37 = Go straight or left 38 = Keep right 39 = Keep left 40 = Roundabout mandatory 41 = End of no passing 42 = End of no passing by vehicles over 3.5 metric tons Citation J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel. The German Traffic Sign Recognition Benchmark: A multi-class classification competition. In Proceedings of the IEEE International Joint Conference on Neural Networks, pages 1453–1460. 2011. @inproceedings{Stallkamp-IJCNN-2011, author = {Johannes Stallkamp and Marc Schlipsing and Jan Salmen and Christian Igel}, booktitle = {IEEE International Joint Conference on Neural Networks}, title = {The {G}erman {T}raffic {S}ign {R}ecognition {B}enchmark: A multi-class classification competition}, year = {2011}, pages = {1453--1460} }
