Traffic Sign Detection Using Generative Adversarial Network: A Comprehensive Review

Abstract

Traffic sign detection is a critical component of advanced driver assistance systems (ADAS) and autonomous vehicles. Traditional computer vision methods have shown limitations in handling challenging conditions such as occlusion, weather variations, and lighting changes. Generative Adversarial Network (GAN) have emerged as a powerful deep learning paradigm that offers novel solutions to these challenges through data augmentation, domain adaptation, and enhanced feature learning. This comprehensive review examines the application of GAN in traffic sign detection, analysing different research papers spanning various GAN architectures, methodologies, and applications. We discuss the fundamental principles of GAN, their evolution in the context of traffic sign recognition, and the specific advantages they bring to this domain. The review categorizes existing research into data augmentation approaches, domain adaptation techniques, detection and classification methods, and hybrid models. We analyse performance metrics, datasets, challenges, and future research directions. It is expected that GAN-based approaches significantly improve detection accuracy under adverse conditions and reduce the dependency on large labelled datasets.