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Home > Archives > Volume 20, No 11 (2022) > Article

DOI: 10.14704/NQ.2022.20.11.NQ66347

AUTOMATIC TRAFFIC SIGN DETECTION and NUMBER PLATE RECOGNIZATIONUSING CNN

Dr. Vicky Nair,,Tejaswini

Abstract

Modern roadways are equipped with traffic signs to alert drivers to hazards including posted speed limits, upcoming road repairs, and pedestrian crossings, among others. This study describes A picture segmentation, traffic sign detection, and input image classification system for real-time Traffic Sign Recognition and classification. For this effect, we use the color boost technique to zero in on the reds in the image. Traffic sign material is identified using Convolutional Neural Networks (CNN) for detection, classification, and recognition. Signs that forewarn drivers of upcoming roadwork, sharp bends, and pedestrian crossings have greatly improved drivers' safety. The three stages of this research are all about recognizing and categorizing traffic signs in real time: image classification, input image segmentation, and traffic sign detection. The colour improvement method is used to isolate the red areas of the picture. Convolutional Neural Networks (CNN), such as Faster R-CNN, Retina Net, YOLO V4, and YOLO V5, are used to detect, classify, and recognise the traffic sign content.

Keywords

Traffic sign detection, traffic sign recognition, convolutional neural network, number plate detection.

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