A Comprehensive Preprocessing and Feature Extraction Methodology for Traffic Sign Detection on Indian Roads Using the IRTSD-Datasetv1

Abstract

Traffic sign detection is a fundamental component of intelligent transportation systems (ITS) and autonomous driving technologies. The accuracy of detection models is heavily dependent on the quality of input data and the effectiveness of preprocessing and feature extraction pipelines. Indian road environments present unique challenges, including diverse sign designs, variable illumination, weather-induced degradation, partial occlusions, and cluttered backgrounds. This paper presents a comprehensive preprocessing and feature extraction framework specifically designed for the Indian Road Traffic Sign Detection Dataset (IRTSD-Datasetv1), which comprises 5,141 images spanning 37 traffic sign classes collected from over 90 cities across India. The proposed framework integrates a multi-stage preprocessing pipeline encompassing image resizing and normalization, Contrast Limited Adaptive Histogram Equalization (CLAHE), Gaussian noise filtering, and morphological operations, followed by an advanced hybrid feature extraction methodology combining Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), colour histogram analysis in HSV space, and Gabor filter-based texture features. The pre-processed dataset is further enriched through a systematic data augmentation strategy incorporating geometric transformations, photometric distortions, and GAN-oriented synthetic sample generation to address class imbalance. Experimental evaluation demonstrates that the proposed preprocessing pipeline achieves a 14.7% improvement in feature discriminability measured through Fisher’s Discriminant Ratio, a 23.2% enhancement in Signal-to-Noise Ratio (SNR), and a 97.8% structural similarity index (SSIM) preservation. When integrated with a baseline Convolutional Neural Network (CNN), the pre-processed features yield an accuracy improvement from 89.3% to 96.7%, confirming the efficacy of the proposed framework. The findings establish a robust foundation for subsequent GAN-based traffic sign detection model development.