Real-Time Traffic Sign Detection and Recognition System Using Deep Learning with CNN and Keras

Abstract

The present invention provides a real-time traffic sign detection and recognition system using Convolutional Neural Networks (CNN) and the Keras deep learning framework. Designed for applications in autonomous vehicles and intelligent transportation systems, the system captures video or image data from vehicle-mounted cameras, preprocesses the data to enhance features, and uses a CNN model to detect and classify traffic signs accurately. The CNN architecture includes convolutional layers for feature extraction, pooling layers for dimensionality reduction, and fully connected layers for classification, with outputs displayed to users or integrated into vehicle control systems. The system is robust against environmental challenges such as lighting variations, occlusions, and degraded signs, and it supports multi-sign detection using object localization techniques. Additionally, it incorporates a retraining mechanism for continuous improvement and adaptability to region-specific traffic sign designs. The inventi

Specification

Description:[001] The present invention relates to the field of autonomous vehicle technology, traffic management, and intelligent transportation systems. Specifically, it pertains to a system and method for detecting and recognizing traffic signs in real-time using deep learning techniques, particularly Convolutional Neural Networks (CNN), implemented through the Keras library.
BACKGROUND OF THE INVENTION
[002] The following description provides the information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[003] Further, the approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior , Claims:1. A system for traffic sign detection and recognition, comprising:
an input module for capturing real-time video or image frames of road environments;
a preprocessing module configured to resize, normalize, and enhance the captured frames;
a Convolutional Neural Network (CNN) implemented using Keras to detect and classify traffic signs within the frames; and
an output module to display recognized traffic signs or provide data to an autonomous vehicle control system.
2. The system as claimed in Claim 1, wherein the preprocessing module includes data augmentation techniques such as rotation, scaling, flipping, and brightness adjustment to enhance model robustness against environmental variations.
3. The system as claimed in Claim 1, wherein the CNN model comprises:
Convolutional layers for extracting hierarchical features from input frames;
Pooling layers for dimensionality reduction and feature preservation;
Fully connected layers for feature integration and classification; and
A softmax output layer to gener

Documents