SMS: SIGNS MAY SAVE - Traffic Sign Recognition and Detection using CNN

• Published in: 2022 6th International Conference on Electronics, Communication and Aerospace Technology pp. 1272 - 1277
• Main Authors: Tumuluru, Praveen, Burra, Lakshmi Ramani, Sunanda, N., Hussain, Shaik Sharez, Madhu, Dudipalli, Varma, Hasthi Venkat
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-12-2022
• Subjects: Autonomous automobiles; Convolution; Convolutional Neural Network; Deep learning; Neural networks; Road safety; Smarter automobiles; Traffic signs; Training; Visualization
• DOI: 10.1109/ICECA55336.2022.10009638

Traffic sign classification automatically detects roadside traffic signs, such as speed limit signs, yield signs, etc. Automatically recognizing traffic signs enables the development of "smarter automobiles." Self-driving automobiles require traffic sign recognition to interpret and comprehend the roadway accurately. Similarly, "driver alert" systems within cars must understand the surrounding roadway to assist and protect drivers. Our automation would assist drivers in detecting and identifying traffic signs without distracting them from the road. With convolution neural networks, the signboards can be accurately classified. The precision can be improved by adding more layers. The GTSRB dataset is utilized here for training and testing; by fine-tuning the parameters, the 43 types of traffic signs are categorized accurately, and the detection speed also increases.