An Efficient Traffic Sign Classification and Recognition with Deep Convolutional Neural Networks
Deepika, Sharda Vashisth, Prabha Sharma · International Journal of convergence in healthcare · 2023
Driver safety assisted driving, and autonomous driving all depend on automated traffic sign recognition. Themost popular deep learning method for recognizing traffic signs is convolutional neural networks. (CNNs).This paper presents a useful technique for automatically detecting images of traffic signs. When employing thetwo common traffic sign photo datasets GTSRB, our method makes use of our CNN model architecture andperforms the best. It aids the driver in safely operating the motor vehicle. The amount of time and effort driversspend manually evaluating and recognizing traffic signs is excessive. This work provides an autonomous trafficsign identification using a convolutional neural network. Our work here introduces a unique CNN architecturewith an Adam optimizer and a batch size of 128 to improve the efficiency of traffic sign recognition. Resultsbased on a complex network were more accurately produced by a convolutional neural network (CNN). With99.81% precision and a minimum of losses, our system learns from the GTSRB dataset, which contains 43 trafficclasses, to identify the correct class of an anonymous traffic sign. The results, however, are better than those ofthe prior research, which examined this approach’s accuracy and efficiency in recognizing traffic signs despitepoor weather and hazy image circumstances.