Traffic sign board recognition is a very significant work for the upcoming driver assistance intelligent vehicle systems. The ability to detect such traffic signs from the real road scenes intensifies the safety of the intelligent vehicle systems. However, automatic detection and classification of traffic signs by such intelligent vehicle systems is a challenging task due to various factors such as variation in light illumination, different viewpoints, colour faded traffic sign, motion blurring, etc. The deep learning models have proved to provide solutions to overcome these factors. This paper proposed deep learning-based residual network for traffic sign detection and classification (DLRN-TSDC) model for effective Indian Traffic Sign Board Recognition. The DLRN-TSDC model makes use of Colour space threshold segmentation technique for the effective identification of sign boards. Simultaneously, pre-processing of the detected traffic sign takes place in three distinct ways such as clipping of edges, image enhancement and size normalization. In addition, the ResNet-50 model is used as a feature extractor and a classifier to determine the final class label of the traffic sign board. Extensive experimental analysis was carried out to validate the effective performance of the DLRN-TSDC model and for the precision, recall, Intersection over Union (IoU) and accuracy scores are 98.76%, 98.92%, 89.56% and 98.84%, respectively.


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    Title :

    A Deep Learning-Based Residual Network Model for Traffic Sign Detection and Classification


    Additional title:

    Smart Innovation, Systems and Technologies




    Publication date :

    2021-10-09


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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