Computer Vision Techniques For Traffic Sign Detection In Adas Applications
Taras Volodymyrovych Kravchenko
Department of Software Tools, National University, Ukraine
Abstract
this paper presents a comprehensive traffic sign recognition system designed to enhance advanced driver assistance systems (ADAS) and autonomous vehicles. The system employs a three-step algorithm comprising color segmentation, shape recognition, and a neural network-based classification to detect and identify various traffic signs in real time. Leveraging the advantages of color-based segmentation for rapid processing and combining it with sophisticated shape detection methods, our approach ensures high accuracy and precision even under challenging conditions such as varying illumination and occlusions. The integration of neural networks allows for effective classification across a broad range of sign types, addressing limitations seen in traditional methods. Our system’s ability to operate with standard onboard cameras, combined with its resilience to lighting variations, marks a significant advancement in traffic sign recognition technology. Extensive testing demonstrates its efficacy in real-world scenarios, highlighting its potential to enhance road safety and support autonomous driving technologies