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  • Open Access

    ARTICLE

    C2Net-YOLOv5: A Bidirectional Res2Net-Based Traffic Sign Detection Algorithm

    Xiujuan Wang1, Yiqi Tian1,*, Kangfeng Zheng2, Chutong Liu3

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1949-1965, 2023, DOI:10.32604/cmc.2023.042224

    Abstract Rapid advancement of intelligent transportation systems (ITS) and autonomous driving (AD) have shown the importance of accurate and efficient detection of traffic signs. However, certain drawbacks, such as balancing accuracy and real-time performance, hinder the deployment of traffic sign detection algorithms in ITS and AD domains. In this study, a novel traffic sign detection algorithm was proposed based on the bidirectional Res2Net architecture to achieve an improved balance between accuracy and speed. An enhanced backbone network module, called C2Net, which uses an upgraded bidirectional Res2Net, was introduced to mitigate information loss in the feature extraction process and to achieve information… More >

  • Open Access

    ARTICLE

    Traffic Sign Recognition for Autonomous Vehicle Using Optimized YOLOv7 and Convolutional Block Attention Module

    P. Kuppusamy1,*, M. Sanjay1, P. V. Deepashree1, C. Iwendi2

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 445-466, 2023, DOI:10.32604/cmc.2023.042675

    Abstract The infrastructure and construction of roads are crucial for the economic and social development of a region, but traffic-related challenges like accidents and congestion persist. Artificial Intelligence (AI) and Machine Learning (ML) have been used in road infrastructure and construction, particularly with the Internet of Things (IoT) devices. Object detection in Computer Vision also plays a key role in improving road infrastructure and addressing traffic-related problems. This study aims to use You Only Look Once version 7 (YOLOv7), Convolutional Block Attention Module (CBAM), the most optimized object-detection algorithm, to detect and identify traffic signs, and analyze effective combinations of adaptive… More >

  • Open Access

    ARTICLE

    Traffic Sign Detection with Low Complexity for Intelligent Vehicles Based on Hybrid Features

    Sara Khalid1, Jamal Hussain Shah1,*, Muhammad Sharif1, Muhammad Rafiq2, Gyu Sang Choi3,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 861-879, 2023, DOI:10.32604/cmc.2023.035595

    Abstract Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians. Consequently, traffic signs have been of great importance for every civilized country, which makes researchers give more focus on the automatic detection of traffic signs. Detecting these traffic signs is challenging due to being in the dark, far away, partially occluded, and affected by the lighting or the presence of similar objects. An innovative traffic sign detection method for red and blue signs in color images is proposed to resolve these issues. This technique aimed to devise an efficient, robust and accurate… More >

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