Nowadays, the number of automobiles in use is rising quickly. Traffic signs and the traffic rules have both increased concurrently. Due to the proliferation of traffic signs, drivers are obliged to get familiar with all of them and face many issues to pay close attention to while driving. Accident rate is also increasing, one of the reasons for accidents is not following traffic signs by the drivers. Drivers may ignore traffic signs on the road because of hazardous weather (fog, heavy rain, etc.) and in order to concentrate on driving, which could be dangerous for both vehicles and pedestrians. Without causing drivers to lose concentration while driving, this software system would assist in detecting and identifying traffic signs on the road side. Driver assistance systems relieve the drivers in performing some tasks and make the driving easy. As well as in intelligent autonomous vehicles, traffic sign recognition is crucial. To decrease traffic accidents and allow for greater driving freedom, a model that can automatically detect and recognize traffic signs on traffic signs is required. This demand is satisfied by traffic sign recognition systems. Application for the detection and recognition of traffic signs is part of this project. Some image processing techniques are employed in this study to find and identify traffic signs. Traffic sign detection and classification are the two components of this architecture. This model suggests many techniques for spotting and identifying traffic sign boards. For the identification and indication of traffic signs, a variety of techniques are used, including color segmentation and RGB to HIS models. Recognition includes things like form context and HOG features. We have achieved higher than 98% recognition accuracy for this system.


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    Title :

    Machine Learning Approach for Traffic Sign Detection and Indication Using Open CV with Python


    Additional title:

    Algorithms for Intelligent Systems



    Conference:

    International Conference on Computer Vision and Robotics ; 2023 ; Lucknow, India February 24, 2023 - February 25, 2023



    Publication date :

    2023-09-28


    Size :

    8 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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