HYBRID APPROACH TO TRAFFIC SIGN RECOGNITION BASED ON COLOR SEGMENTATION AND CONVOLUTIONAL NEURAL NETWORKS
This paper presents a hybrid approach to traffic sign recognition that combines classical preprocessing techniques (color segmentation, contour detection, Haar Cascade, and HOG) with a lightweight Convolutional Neural Network (CNN) for classification. The proposed method reduces the amount of processed image data by a factor of 10–20, as only preselected regions of interest are passed to the neural network.