A Convolutional Approach to Traffic Sign Detection

With the notion of smart cities transforming cities into digital societies and making people's lives easier in every way, Intelligent Transportation Systems have become an integral element among all. The Intelligent Transportation System (ITS) attempts to improve traffic efficiency by reducing congestion and ensuring the safety and comfort of commuters in real time. Traffic sign detection and recognition is one of the multifaceted conjunctive fields of research in ITS. In this paper, we address the issue of the TSR (traffic sign recognition) problem, i.e., classification of traffic signs along the roadside which plays a crucial role in developing advanced driver assistance and autonomous driving systems. CNN's network design has a huge impact on its performance and convergence. As a result, we use the Genetic Algorithm (GA) to automate the task of selecting a high-performance CNN (Convolutional Neural Network) Architecture for the GTSRB (German Traffic Sign Recognition Benchmark) dataset. The model is optimized through GA using multiple network configurations in the search space. Our model takes into account the limitations of the dataset, and we use certain data augmentation approaches to address the issues. We were able to attain an average accuracy of 98.2% which demonstrates the state-of-the-art performance on the publicly available dataset.KeywordsConvolutional neural networkGenetic algorithmMulticlass classificationDeep learningModel optimization

Objectives: To present an automated computer vision-based traffic sign detection model created to identify roadside signs accurately in different environmental conditions. The goal is to improve detection accuracy using a lightweight architecture suitable for mobile devices. Methods: This study presents CV-TSD model. The technique integrates Histogram of Oriented Gradients (HoG), Local Binary Pattern (LBP), and TensorFlow-based machine learning for image recognition. Traffic sign datasets are collected from various online fora, annotated, and trained using convolutional neural networks. The proposed system uses Histogram of Oriented Gradients (HoG), Local Binary Pattern (LBP), and deep learning with TensorFlow for effective image classification. A custom dataset of traffic sign images was collected from several online sources, manually labelled, and used to retrain a convolutional neural network using transfer learning. Three environmental categories—dark shade, cloudy, and rainy—were used to test classification robustness. We assessed performance with training accuracy, validation accuracy, and cross-entropy loss. Findings: Experimental results show that CV-TSD consistently outperforms traditional HoG and LBP methods across all metrics. The system achieves a maximum training accuracy of 98% and a validation accuracy of 97% with a limited number of image samples. Furthermore, the model keeps stable cross-entropy values, indicating effective learning without overfitting. The lightweight design allows reliable performance even with smaller datasets, making it ideal for mobile and intelligent transportation systems. Novelty: Unlike standard traffic sign detection methods, CV-TSD mimics human visual perception by merging semantic image understanding with deep learning. Its ability to classify accurately with minimal data and low computational demands marks a significant advancement for real-time applications. Keywords: Traffic Sign Detection, Deep Learning, Image Classification, TensorFlow, HoG, LBP, Transfer Learning

Indian traffic sign detection and recognition using deep learning

Automatic traffic sign detection and recognition has achieved good results using convolutional neural networks. Novel architectures are still being proposed in order to improve accuracy of detection and segmentation of traffic sings. In this paper, we are examining the possibility for traffic sign detection and recognition in real-time. For that purpose, we employed a novel YOLOv3 architecture, which has been proven to be fast and accurate method for object detection. It was shown that real-time detection can be achieved, even on HD images, with mAP above 88%.

Autonomous vehicles are one of the most discussed technologies right now since they have the potential to completely transform the mobility industry. There is a lot of research being done to make it more efficient. Autonomous vehicle models have to overcome obstacles such as lane detection, traffic light recognition, traffic sign detection, navigation, etc. The performance of autonomous cars depends on a variety of factors, and detection of traffic signs is one of them, as the safety of driving is heavily reliant on the identification of traffic signals. Traffic signs provide critical information about vehicle safety, such as traffic conditions, warning messages, road lights, and driving routes. This problem can be solved and safe driving can be ensured with a solid AI model for traffic sign detection and recognition. There are multiple ways through which this issue can be solved using various methods such as machine learning and deep learning. The main aim of the paper is to compare the different techniques such as the Local Binary Pattern (LBP), Convolutional Neural Network (CNN), and Transfer Learning (TL).

Vision-based traffic sign detection plays a crucial role in intelligent transportation systems. Recently, many approaches based on deep learning for traffic sign detection have been proposed and showed better performance compared with traditional approaches. However, due to difficult conditions in driving environment and the size of traffic signs in traffic scene images, the performance of deep learning-based methods on small traffic sign detection is still limited. In addition, the inference speed of current state-of-the-art approaches on traffic sign detection is still slow. This paper proposes a deep learning-based approach to improve the performance of small traffic sign detection in driving environments. First, a lightweight and efficient architecture is adopted as the base network to address the issue of the inference speed. To enhance the performance on small traffic sign detection, a deconvolution module is adopted to generate an enhanced feature map by aggregating a lower-level feature map with a higher-level feature map. Then, two improved region proposal networks are used to generate proposals from the highest-level feature map and the enhanced feature map. The proposed improved region proposal network is designed for fast and accuracy proposal generation. In the experiments, the German Traffic Sign Detection Benchmark dataset is used to evaluate the effectiveness of each enhanced module, and the Tsinghua-Tencent 100K dataset is used to compare the effectiveness of the proposed approach with other state-of-the-art approaches on traffic sign detection. Experimental results on Tsinghua-Tencent 100K dataset show that the proposed approach achieves competitive performance compared with current state-of-the-art approaches on traffic sign detection while being faster and simpler.

Intelligent Transportation Systems (ITS) have progressively incorporated machine learning to optimize traffic efficiency, enhance safety, and improve real-time decision-making. However, the traditional centralized machine learning (ML) paradigm faces critical limitations regarding data privacy, scalability, and single-point vulnerabilities. This study explores FL as a decentralized alternative that preserves privacy by training local models without transferring raw data. Based on a systematic literature review encompassing 39 ITS-related studies, this work classifies applications according to their architectural detail—distinguishing systems from models—and identifies three families of federated learning (FL) frameworks: privacy-focused, integrable, and advanced infrastructure. Three representative frameworks—Federated Learning-based Gated Recurrent Unit (FedGRU), Digital Twin + Hierarchical Federated Learning (DT + HFL), and Transfer Learning with Convolutional Neural Networks (TFL-CNN)—were comparatively analyzed against a client–server baseline to assess their suitability for ITS adaptation. Our qualitative, architecture-level comparison suggests that DT + HFL and TFL-CNN, characterized by hierarchical aggregation and edge-level coordination, are conceptually better aligned with scalability and stability requirements in vehicular and traffic deployments than pure client–server baselines. FedGRU, while conceptually relevant as a meta-framework for coordinating multiple organizational models, is primarily intended as a complementary reference rather than as a standalone architecture for large-scale ITS deployment. Through application-level evaluations—including traffic prediction, accident detection, transport-mode identification, and driver profiling—this study demonstrates that FL can be effectively integrated into ITS with moderate architectural adjustments. This work does not introduce new experimental results; instead, it provides a qualitative, architecture-level comparison and adaptation guideline to support the migration of ITS applications toward federated learning. Overall, the results establish a solid methodological foundation for migrating centralized ITS architectures toward federated, privacy-preserving intelligence, in alignment with the evolution of edge and 6G infrastructures.

TSD (Traffic Sign Detection) is a hotspot in autonomous driving and assisted driving research. TSD research is of great significance for improving road traffic safety. In recent years, CNN (Convolutional Neural Networks) have achieved great success in object detecting tasks. It shows better accuracy or faster execution speed than traditional method. However, the execution speed and the detection accuracy of the existing CNN methods cannot be obtained at the same time. What's more, the hardware requirements are also higher than before, resulting in a larger detection cost. In order to solve these problems, this paper proposes an improved CNN model based on YOLO model, darknet 53 construction. By introducing batch normalization and RPN networks and improving the network structure for traffic sign detection tasks, the YOLO neural network detection model is optimized. The accuracy of the model in the traffic sign detection task is greatly improved, and the detection speed becomes faster. The results show that the method in this paper is of great help to improve the accuracy and detection speed of traffic sign detection and reduce the hardware requirements of the detection system as well.

Traffic sign detection plays an important role in a traffic sign recognition system. It is also an unsolved problem in the intelligence transportation system. Two main technique routes are typically used in current research of the traffic sign detection: the technique using gray-level object image or that using color object image. The former has the advantage that the related theories and methods are more mature, but the weakness is short of information, which results in more chances of misdetection. The latter has the advantage that more abundant information can be provided same as human sense of vision, but the weakness is that the related theories and methods are still immaturity. This research proposes a concept of color-shape pair based on the prior characteristic of the color and the geometric shape by analyzing the traffic signs that might appear in Chinese highways. Also, a model of traffic signs, color-geometric model, is given according to the relative definition of the Color-Shape Pair. This model is constituted with 5 kinds of basic shapes and 3 kinds of basic colors of the traffic signs, focusing on the unique determinacy that is the most important characteristics of color and geometric shape. Therefore, using the detection of traffic signs based on color-geometric model, we can realize the rough classification of the traffic signs. Furthermore, we can divide 116 kinds of traffic signs on Chinese road into 7 subclasses, and reduce the complexity of recognition system of road traffic signs. The simulation experiment results have shown that this method can achieve 100% correct ratio for the traffic sign detection and the rough classification, and the robustness and real-time performance are also improved.

Driver drowsiness is a major cause of road accidents worldwide, leading to thousands of injuries and fatalities each year. Detecting early signs of fatigue remains a critical challenge in road safety and intelligent transportation systems. This paper proposes a novel deep learning-based framework named SAFE-DRIVE-AI, designed to detect drowsiness in real-time by analyzing visual cues from the driver's eyes. The proposed framework integrates three components: A Convolutional Neural Network (CNN) to extract spatial features from eye images, a Long Short-Term Memory (LSTM) layer for capturing temporal patterns such as blink duration and frequency, and an Attention mechanism to enhance the model’s focus on the most relevant time steps. To validate the effectiveness of Deep Learning (DL) techniques, a comparative analysis was conducted using both traditional Machine Learning (ML) models (Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines) and DL models (CNN, LSTM). The experimental results showed that the DL models significantly outperformed the ML ones in all metrics. The hybrid CNN+LSTM model achieved an accuracy of 97.3% and an F1-score of 96.8%, being the most accurate approach. SAFE-DRIVE-AI is designed for real-world deployment, with potential integration into edge devices, 5G-enabled systems, and real-time alert mechanisms. The proposed method offers a practical and intelligent solution for enhancing driver safety through proactive fatigue monitoring.

Traffic sign detection and classification is a critical component of intelligent transportation systems, which is applied to inform automatic unmanned driving systems and driving assistance systems about conditions and limits of roads. Although computer vision is widely utilised in traffic sign detection, detection and recognising traffic signs globally remains a great challenge due to the variety of sign types, scale‐variance and geometric variations. To address these problems, this study proposes a region‐based deep convolutional neural network (CNN) framework for traffic sign detection and classification. Specifically, a multi‐branch sample pyramid module is proposed, which is based on multi‐branch CNNs for multi‐scaled feature exaction. A limited deformable convolutional module is then embedded into the CNN layers to learn the distorted information representation for deformation handing. Moreover, a scale‐aware multi‐task region proposal network module is applied to detect traffic signs with various scales. The whole network is trained in an end‐to‐end manner. Finally, experiments are conducted on two public detection data sets to demonstrate the effectiveness of the proposed method.

Recent trends in the development of autonomous vehicles focus on real-time processing of vast amounts of data from various sensors. The data can be acquired using multiple cameras, lidars, ultrasonic sensors, and radars to collect useful information about the state of the traffic and the surroundings. Significant computational power is required to process the data fast enough, and this is even more pronounced in vehicles that not only assist the driver but are capable of fully autonomous driving. This article proposes speed and accuracy improvement of traffic sign detection and recognition in high-definition images, based on focusing on different regions of interest in traffic images. These regions are determined with efficient and parallelized preprocessing of every traffic image, after which convolutional neural network is applied for detection and recognition in parallel on graphics processing units. We employed different “You Only Look Once” (YOLO) architectures as baseline detectors, due to their speed, straightforward architecture, and high accuracy in general object detection tasks. Several preprocessing procedures were proposed, to achieve real-time performance requirement. Our experiments using a large-scale traffic sign dataset show that we can achieve real-time detection in high-definition images with high recognition accuracy.

Advanced driver assistance systems (ADASs) and autonomous vehicles are expected to increase safety, lower energy and fuel consumption, and lower pollutants from road traffic. The advanced driver assistance system’s major features include lane detection and tracking. Finding color line marks on the road is the technique of lane detection. The process of lane tracking aims to help the vehicle continue traveling along a predetermined course. Hence, automatic detection of lanes using convolutional neural networks (CNNs) models has gained popularity in the current economic development. This paper also aims at detecting various objects using convolutional neural networks. In this work, we offer object classification and detection, a demanding topic in computer vision and image processing. As a result, we deployed convolutional neural networks on the Keras platform with TensorFlow support. The experimental results illustrate the amount of time needed to train, test, and generate the model using a constrained computing environment. Here, we trained the system for about 80 images which have taken a couple of seconds to detect with better accuracy. Traffic sign recognition is carried out, which is a significant area of research in ADAS. It is crucial for driverless vehicles and is frequently used to read stationary or moving road signs along the side of the road. A comprehensive recognition system is made up of traffic sign detection (TSD) and categorization (TSC). The paper also aimed at traffic sign recognition, which is crucial to consider, because traffic sign recognition is typically applied to portable devices. The model’s detection accuracy is ensured as long as the speed is maintained. The model developed in this study is 99.89% accurate.

With the rapid development of the Internet of Vehicles (IoV), traffic sign detection plays an indispensable role in advancing autonomous driving and intelligent transportation. However, current road traffic sign detection technologies face challenges in terms of information privacy protection, model accuracy verification, and result sharing. To enhance system sustainability, this paper introduces blockchain technology. The decentralized, tamper-proof, and consensus-based features of blockchain ensure data privacy and security among vehicles while facilitating trustworthy validation of traffic sign detection algorithms and result sharing. Storing model training data on distributed nodes reduces the system computational resources, thereby lowering energy consumption and improving system stability, enhancing the sustainability of the model. This paper introduces an enhanced GGS-YOLO model, optimized based on YOLOv5. The model strengthens the feature extraction capability of the original network by introducing a coordinate attention mechanism and incorporates a BiFPN feature fusion network to enhance detection accuracy. Additionally, the newly designed GGS convolutional module not only improves accuracy but also makes the model more lightweight. The model achieves an enhanced detection accuracy rate of 85.6%, with a reduced parameter count of 0.34×107. In a bid to broaden its application scope, we integrate the model with blockchain technology for traffic sign detection in the IoV. This method demonstrates outstanding performance in traffic sign detection tasks within the IoV, confirming its feasibility and sustainability in practical applications.

The introduction to autonomous vehicles and intelligent transportation systems, as well as related work on autonomous vehicles and intelligent transportation systems, are described in this chapter. Aside from that, it has a chapter on autonomous intelligent vehicles and autonomous vehicles. Issues and challenges in autonomous intelligent vehicles, as well as the framework of an intelligent transportation system. In addition, the chapter discusses research opportunities in autonomous intelligent vehicles, IPv4 and IPv6 issues, and the future of IPv9 for vehicles, as well as a survey on autonomous vehicles in highway scenarios. During the last century, the automobile industry has excelled at producing cars that are both safe and affordable. Because of significant advancements in the Internet of things (IoT), the programmable logic controller (PLC), and other aspects of computing, autonomous vehicles (AVs) are becoming a reality. Cars, trucks, and other vehicles that don’t need to be plugged in to get power are referred to as self-sufficient vehicles. The multi-agent-based transportation system, which demonstrates how vehicles, drivers, roads, infrastructure, and vehicles all work together, is one of the most important intelligent transportation systems because it demonstrates how all of these things work together. To solve and manage traffic problems, an intelligent transportation systems (ITS) system employs a variety of technologies. Communication and control, vehicle sensing, and electronics are among these technologies. ITS has been used in the developed world for the past two decades. It is still a novel concept in the developing world. Sensors and computers work together in the Internet of things to store and process data, and data analytics is used to make the traffic system run more smoothly.KeywordsAutonomous vehicle (AV)Intelligent transportation system (ITS)Autonomous intelligent vehicle (AIV)Advanced driver assistance system (ADAS)Advanced driving system (ADS)