Bingyi Ren^1,4, Juwei Zhang^2,3,4,*, Tong Wang^2,4

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1425-1440, 2024, DOI:10.32604/cmc.2024.052667 - 18 July 2024

Abstract Autonomous driving technology has entered a period of rapid development, and traffic sign detection is one of the important tasks. Existing target detection networks are difficult to adapt to scenarios where target sizes are seriously imbalanced, and traffic sign targets are small and have unclear features, which makes detection more difficult. Therefore, we propose a Hybrid Feature Fusion Traffic Sign detection algorithm based on YOLOv7 (HFFT-YOLO). First, a self-attention mechanism is incorporated at the end of the backbone network to calculate feature interactions within scales; Secondly, the cross-scale fusion part of the neck introduces a… More >

Xiujuan Wang¹, Yiqi Tian^1,*, Kangfeng Zheng², Chutong Liu³

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1949-1965, 2023, DOI:10.32604/cmc.2023.042224 - 29 November 2023

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… More >

P. Kuppusamy^1,*, 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 - 31 October 2023

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… More >

Sara Khalid¹, Jamal Hussain Shah^1,*, Muhammad Sharif¹, Muhammad Rafiq², Gyu Sang Choi^3,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 861-879, 2023, DOI:10.32604/cmc.2023.035595 - 08 June 2023

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… More >