Hui Chen¹, Mohammed A. H. Ali^1,*, Bushroa Abd Razak¹, Zhenya Wang², Yusoff Nukman¹, Shikai Zhang¹, Zhiwei Huang¹, Ligang Yao³, Mohammad Alkhedher⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-26, 2026, DOI:10.32604/cmc.2025.067286 - 09 December 2025

Abstract Accurate and real-time traffic-sign detection is a cornerstone of Advanced Driver-Assistance Systems (ADAS) and autonomous vehicles. However, existing one-stage detectors miss distant signs, and two-stage pipelines are impractical for embedded deployment. To address this issue, we present YOLO-SMM, a lightweight two-stage framework. This framework is designed to augment the YOLOv8 baseline with three targeted modules. (1) SlimNeck replaces PAN/FPN with a CSP-OSA/GSConv fusion block, reducing parameters and FLOPs without compromising multi-scale detail. (2) The MCA model introduces row- and column-aware weights to selectively amplify small sign regions in cluttered scenes. (3) MPDIoU augments CIoU loss… More >

Qing Guo^1,2, Juwei Zhang^1,2,3,*, Bingyi Ren^1,2

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069053 - 10 November 2025

Abstract Traffic sign detection is an important part of autonomous driving, and its recognition accuracy and speed are directly related to road traffic safety. Although convolutional neural networks (CNNs) have made certain breakthroughs in this field, in the face of complex scenes, such as image blur and target occlusion, the traffic sign detection continues to exhibit limited accuracy, accompanied by false positives and missed detections. To address the above problems, a traffic sign detection algorithm, You Only Look Once-based Skip Dynamic Way (YOLO-SDW) based on You Only Look Once version 8 small (YOLOv8s), is proposed. Firstly,… More >

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 >

Mohamed Karray^1,*, Nesrine Triki^2,*, Mohamed Ksantini²

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 119-138, 2024, DOI:10.32604/cmc.2024.051562 - 18 July 2024

Abstract Advanced Driver Assistance Systems (ADAS) technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road. Traffic Sign Recognition System (TSRS) is one of the most important components of ADAS. Among the challenges with TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time. Accordingly, this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules. Firstly, the Speed Limit Detection (SLD) module uses… 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 >

Zeyu Zhang¹, Han Zhu¹, Wei Zhang¹, Zhiming Cai^2,*, Linkai Zhu², Zefeng Li²

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1901-1917, 2023, DOI:10.32604/csse.2023.039395 - 28 July 2023

Abstract With the rapid development of urban road traffic and the increasing number of vehicles, how to alleviate traffic congestion is one of the hot issues that need to be urgently addressed in building smart cities. Therefore, in this paper, a nonlinear multi-objective optimization model of urban intersection signal timing based on a Genetic Algorithm was constructed. Specifically, a typical urban intersection was selected as the research object, and drivers’ acceleration habits were taken into account. What’s more, the shortest average delay time, the least average number of stops, and the maximum capacity of the intersection… More >

Qiang Luo¹, Wenbin Zheng^1,2,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2227-2246, 2023, DOI:10.32604/iasc.2023.040195 - 21 June 2023

Abstract In the field of traffic sign recognition, traffic signs usually occupy very small areas in the input image. Most object detection algorithms directly reduce the original image to a specific size for the input model during the detection process, which leads to the loss of small object information. Additionally, classification tasks are more sensitive to information loss than localization tasks. This paper proposes a novel traffic sign recognition approach, in which a lightweight pre-locator network and a refined classification network are incorporated. The pre-locator network locates the sub-regions of the traffic signs from the original… 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 >

Kha Tu Huynh¹, Thi Phuong Linh Le¹, Muhammad Arif², Thien Khai Tran^3,*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 401-418, 2023, DOI:10.32604/iasc.2023.036981 - 29 April 2023

Abstract To pursue the ideal of a safe high-tech society in a time when traffic accidents are frequent, the traffic signs detection system has become one of the necessary topics in recent years and in the future. The ultimate goal of this research is to identify and classify the types of traffic signs in a panoramic image. To accomplish this goal, the paper proposes a new model for traffic sign detection based on the Convolutional Neural Network for comprehensive traffic sign classification and Mask Region-based Convolutional Neural Networks (R-CNN) implementation for identifying and extracting signs in More >