Real-Time Traffic Sign Detection and Recognition System using Computer Vision and Machine Learning

Indian traffic sign detection and recognition using deep learning

In this paper, we propose a traffic sign recognition algorithm based on support vector machine (SVM) and convolutional neural network (CNN) for the traffic sign recognition of intelligent transportation and unmanned vehicle. In the detection of traffic signs, by using color features, firstly we convert the RGB color space to HSV color space, which can get the regions of interest (ROI). Then extract the Histogram of Oriented Gradient (HOG) features, and determine whether it is traffic sign by support vector machine (SVM). This algorithm can successfully detect traffic signs with a high detecting rate. The following is the classification of the detected traffic signs, by using a kind of convolutional neural network, which fuse the RGB information of a picture. In the end, the `German traffic signs' dataset is used to verify the accuracy. Experimental results show that the proposed algorithm can effectively recognize traffic signs, achieving high accuracy and the algorithm complexity is lower.

The objective of this chapter is to detect and recognize the traffic signs using two methods. Traffic signs are silent speakers to the road users. It gives advance information about road conditions ahead. In this chapter, an effective method is proposed to identify the traffic signs in an automated manner. The traffic sign recognition for advanced driver-assistance system of Method I comprises of three stages: (1) detection using Haar-like features with AdaBoost method, (2) feature extraction using speeded up robust features, and (3) classification using support vector machine. Method II comprises of two stages: (1) detection of traffic sign using convolution neural network and (2) classification using convolution neural network. The performance of the system is analyzed. From the experimental results, it is observed that the Method II using convolution neural network gives a better performance compared with Method I

Traffic sign recognition systems (TSR) are a hot research topic because of their importance in the field of driver safety. Major car companies are developing smart driving systems for this purpose. TSR was introduced in 2022 in some luxury cars, such as the Cadillac XT4 and Mercedes-Benz GLS. TSR is a driver assistance feature that can be used to advise and warn the driver about possible road limitations along the current route. The development of the TSR system aims at two objectives; the first is to develop a fast and inexpensive system that can be added to economic vehicles. The second objective is to apply safe leadership automatically when accessing a fast and reliable TSR system. Following laws can be made compulsory by the vehicle itself, such as not exceeding speed in the event of a speed limit. (except in emergencies).There are many issues and challenges facing the researcher that works on the detection and recognition of traffic signs, such as bad weather, various illumination, occlusion, spped of cars.This research paper aims to present a proposal that facilitates access to a fast, reliable, and low-cost system. The proposed TSR is based on combining the You Only Lock Once (YOLOv5) network with a convolution neural network (CNN) that enhances the accuracy of detection and recognition in addition to speeding up the processing. Two datasets were used for training models. The YOLOv5 was trained on the GTSDB, while CNN was trained on the GTSRB.YOLOv5 is used to detect the traffic signs in the input image. The detected signs are bounded by boxes and labeled according to the group that belongs to them (the dataset is clustered into four groups (prohibitory, mandatory, danger, and others)), and then each bounded box is segmented from the rest of the image background. The segmented image (including the traffic sign) is input to the CNN to be classified into one of the 43 traffic signs.This proposed system achieved an average of 94% detection accuracy by using YOLO and 99.95% classification accuracy in CNN, with a processing time of 0.031 seconds per frame. In real-time applications, this low computation time can improve the system's stability and dependability.

A Convolutional Approach to Traffic Sign Detection presents a deep learning-based method using Convolutional Neural Networks (CNNs) for accurate traffic sign detection and classification. The system follows a two-stage pipeline: region proposal and sign classification, leveraging data augmentation and transfer learning for improved performance. It demonstrates robustness to challenges like poor lighting and occlusion and supports real-time operation, making it suitable for ADAS and autonomous vehicles. Experimental results show high accuracy across datasets, highlighting its potential in enhancing road safety and intelligent transportation systems. Index Terms — Traffic Sign Detection, Convolutional Neural Networks (CNN), Deep Learning, Computer Vision, GTSRB, Image Classification, Object Detection, Real-Time Detection, Autonomous Driving, Data Augmentation, OpenCV, Keras, TensorFlow, Image Preprocessing, Road Safety

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.

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

The issue of traffic congestion is a significant problem that affects numerous cities around the world, including the city of Caloocan in the Philippines. With the increasing number of vehicles on the roads and the limited infrastructure to support this growth, traffic management has become a critical concern for the city government. In order to address this issue effectively, it is essential to implement appropriate strategies and plans that will help alleviate traffic congestion and improve overall road safety. One key aspect of effective traffic management is the development of a comprehensive training plan for traffic enforcers and personnel in Caloocan City. This plan will serve as the basis for equipping traffic officers with the necessary skills and knowledge to effectively manage traffic flow, enforce road rules, and ensure the safety of motorists and pedestrians. By providing comprehensive training, the city government will be able to improve the efficiency and effectiveness of its traffic management strategies, ultimately leading to a smoother and safer traffic flow throughout the city. The proposed training plan for traffic management in Caloocan City will encompass a wide range of topics, including but not limited to traffic rules and regulations, traffic signal management, accident response and management, public relations and communication skills, and the use of technology in traffic management. By providing training in these key areas, traffic enforcers will be better equipped to handle the various challenges and situations they may encounter on the road, leading to improved traffic management outcomes in the city. In addition to providing training on specific traffic management skills, the proposed training plan will also focus on enhancing the professionalism and integrity of traffic enforcers in Caloocan City. By instilling a sense of duty, responsibility, and accountability among traffic officers, the city government will be able to improve the overall quality of traffic management services provided to the public. This, in turn, will help build trust and confidence among motorists and pedestrians, leading to a more harmonious and cooperative relationship between the community and traffic enforcers.

Detection of traffic signs plays an important role in autonomous driving, traffic surveillance and traffic safety. Previous research in Traffic Sign Detection (TSD) generally focused on traffic signs which are over the roads, the traffic signs on road surface have not been discussed. In this paper, we propose a road surface traffic sign detection system by applying convolutional neural network (CNN). The proposed system consists of two main stages: 1) a hybrid region proposal method to hypothesize the traffic sign locations by taking into account complementary information of color and edge; 2) feature extraction, classification, bounding box regression and non-maximum suppression by Fast R-CNN. Extensive experiments have been conducted using our field-captured dataset, demonstrating outstanding performance with regard to high recall and precision rate. The overall average precision (AP) is about 85.58%.

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).

Traffic signs detection is a hot and important topic in the research field of computer vision and lots of applications, e.g., driver assistance system and autonomous vehicle, etc. Thanks to the development of the convolutional neural network, object detection has achieved promising results in recent years. Nevertheless, traffic sign detection still faces two difficulties due to the characteristics of traffic signs. The first difficulty is the detection of small and blurred traffic signs. The second difficulty is that training samples are sparse and imbalanced. For tackling these two difficulties, Guided Region Enlarging Algorithm (GREA) is proposed in the paper. The GREA consists of two sub-modules, Potential region estimation network (PREN) and NMS-Cropping. The PREN obtains a potential object region of each input image first. Then, the potential object region is enlarged by the NMS-Cropping algorithm for subsequent augmenting training. The GREA makes use of features of a small and blurred traffic sign to enhance the performance of a traffic sign detector. To comprehensively evaluate the proposed approach, two traffic sign datasets include the Swedish traffic sign data set and the Tsinghua-Tencent 100k data set are utilized in the experiment. Experimental results demonstrate the efficiency of the proposed GREA, and our approach is comparable to the state-of-the-art approaches.

Traffic sign detection is a crucial aspect of advanced driver assistance systems (ADAS) for academic research and the automotive industry. seeing that accurate and timely detection of traffic signs (TS) is essential for ensuring the safety of driving. However, TS detection methods encounter challenges like slow detection speed and a lack of robustness in complex environments. This paper suggests addressing these limitations by proposing the use of the you only look one version 7 (YOLOv7) network to detect and recognize TS in road scenes. Furthermore, the k-means++ algorithm is used to acquire anchor boxes. Additionally, a tiny version of YOLOv7 is used to take advantage of its real-time and low model size, which are required for real-time hardware implementation. So, we conducted an experiment using our proprietary Morocco dataset. According to the experimental results, YOLOv7 achieves 85% in terms of mean average precision (mAP) at 0.5 for all classes. And YOLOv7-tiny obtains 90% in the same term. Afterward, a recognition system for the prohibitive class using the convolutional neural network (CNN) is trained and integrated inside the YOLOv7 algorithm; its model achieves an accuracy of 99%, which leads to a good specification of the prohibitive sign meaning.

Traffic safety remains a pressing global concern, with traffic signs playing a vital role in regulating and guiding drivers. However, environmental factors like lighting and weather often compromise their visibility, impacting human drivers and autonomous vehicle (AV) systems. This study addresses critical traffic sign detection (TSD) and classification (TSC) gaps by leveraging the YOLOv8 algorithm to evaluate the detection accuracy and sign quality under diverse lighting conditions. The model achieved robust performance metrics across day and night scenarios using the novel ZND dataset, comprising 16,500 labeled images sourced from the GTSRB, GitHub repositories, and real-world own photographs. Complementary retroreflectivity assessments using handheld retroreflectometers revealed correlations between the material properties of the signs and their detection performance, emphasizing the importance of the retroreflective quality, especially under night-time conditions. Additionally, video analysis highlighted the influence of sharpness, brightness, and contrast on detection rates. Human evaluations further provided insights into subjective perceptions of visibility and their relationship with algorithmic detection, underscoring areas for potential improvement. The findings emphasize the need for using various assessment methods, advanced algorithms, enhanced sign materials, and regular maintenance to improve detection reliability and road safety. This research bridges the theoretical and practical aspects of TSD, offering recommendations that could advance AV systems and inform future traffic sign design and evaluation standards.

In recent years, the deep learning is applied to the field of traffic sign detection methods which achieves excellent performance. However, there are two main challenges in traffic sign detection to be solve urgently. For one thing, some traffic signs of small size are more difficult to detect than those of large size so that the small traffic signs are undetected. For another, some false signs are always detected because of interferences caused by the illumination variation, bad weather and some signs similar to the true traffic signs. Therefore, to solve the undetection and false detection, we first propose a cascaded R-CNN to obtain the multiscale features in pyramids. Each layer of the cascaded network except the first layer fuses the output bounding box of the previous one layer for joint training. This method contributes to the traffic sign detection. Then, we propose a multiscale attention method to obtain the weighted multiscale features by dot-product and softmax, which is summed to fine the features to highlight the traffic sign features and improve the accuracy of the traffic sign detection. Finally, we increase the number of difficult negative samples for dataset balance and data augmentation in the training to relieve the interference by complex environment and similar false traffic signs. The data augment method expands the German traffic sign training dataset by simulation of complex environment changes. We conduct numerous experiments to verify the effectiveness of our proposed algorithm. The accuracy and recall rate of our method are 98.7% and 90.5% in GTSDB, 99.7% and 83.62% in CCTSDB and 98.9% and 85.6% in Lisa dataset respectively.