Contents
Traffic Signs Detection Using Matlab
Abstract:
The project “Traffic Signs Detection Using Matlab” focuses on the development of a system that utilizes image processing techniques to detect and recognize traffic signs. By applying effective algorithms and image processing steps, the system aims to detect traffic signs in input images and display the corresponding sign names as output. This project incorporates various stages, such as image conversion, noise removal, template matching, and optical character recognition, to achieve accurate detection and recognition of traffic signs. The system provides results with an accuracy ranging from 60% to 80%.
Introduction:
Traffic signs ensure road safety and guide drivers. Traffic sign-detecting automated systems can improve road safety. “Traffic Signs Detection Using Matlab” uses image processing to automate traffic sign detection and recognition.
Objective:
The main objective of this project is to develop a system that can accurately detect and recognize traffic signs in images. By implementing various image processing steps and algorithms, the system aims to provide real-time identification of traffic signs and display their corresponding names as output.
Project Details:
The project begins by converting the input RGB traffic sign image into a grayscale image. This grayscale image is further processed to obtain a black and white representation, which facilitates subsequent image processing steps. Since captured images may contain noise and environmental interference, the system incorporates filtering techniques to remove such disturbances and improve the accuracy of sign detection.
The system utilizes a directory to store a dataset of template images of traffic signs. These template images have standardized width and height and are in black and white format. When a user uploads a traffic image, the system applies image pre-processing steps and filtering techniques to enhance the image quality. Then, a comparison is made between the dataset values and the values of the query image. Based on the similarity, the system determines the traffic sign and displays the result in text format.
To recognize the traffic signs, the project incorporates the concept of optical character recognition (OCR). This technique enables the system to identify and extract text information present on the traffic signs, further improving the accuracy of the detection and recognition process. The system achieves results with an accuracy ranging from 60% to 80%, providing valuable assistance in traffic sign identification.
Advantages:
- Enhanced Road Safety: The system aids in the automated detection and recognition of traffic signs, contributing to improved road safety by providing real-time sign identification.
- Efficient Image Processing: By utilizing image processing techniques and algorithms, the system effectively processes and analyzes traffic sign images to facilitate accurate detection.
- Standardized Dataset: The use of a standardized dataset of template images allows for consistent and reliable comparison with query images, improving the accuracy of sign recognition.
Conclusion:
The project “Traffic Signs Detection Using Matlab” presents an innovative solution for automated traffic sign detection and recognition. By incorporating image processing steps, filtering techniques, template matching, and optical character recognition, the system achieves accurate results in identifying traffic signs. The system’s ability to provide real-time sign detection and display their corresponding names contributes to improved road safety and facilitates efficient traffic management. With an accuracy ranging from 60% to 80%, the project demonstrates the effectiveness of image processing techniques in traffic sign detection using Matlab.
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