Image Classification using CNN for Traffic Signs in Pakistan. (arXiv:2102.10130v1 [cs.CV])

The autonomous automotive industry is one of the largest and most
conventional projects worldwide, with many technology companies effectively
designing and orienting their products towards automobile safety and accuracy.
These products are performing very well over the roads in developed countries.
But can fail in the first minute in an underdeveloped country because there is
much difference between a developed country environment and an underdeveloped
country environment. The following study proposed to train these Artificial
intelligence models in environment space in an underdeveloped country like
Pakistan. The proposed approach on image classification uses convolutional
neural networks for image classification for the model. For model pre-training
German traffic signs data set was selected then fine-tuned on Pakistan’s
dataset. The experimental setup showed the best results and accuracy from the
previously conducted experiments. In this work to increase the accuracy, more
dataset was collected to increase the size of images in every class in the data
set. In the future, a low number of classes are required to be further
increased where more images for traffic signs are required to be collected to
get more accuracy on the training of the model over traffic signs of Pakistan’s
most used and popular roads motorway and national highway, whose traffic signs
color, size, and shapes are different from common traffic signs.



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