Traffic Signs Classification using Convolutional Neural Networks: A review

Indian traffic sign detection and recognition using deep learning

To avoid road accidents and for safe driving, a proper traffic sign recognition and classification is necessary. It is also an important part of intelligent transport systems and driverless vehicles. This article outlines the different ways to recognize and classify traffic signs. Various types of algorithms are used to recognize text-based, shape-based and color-based traffic signs. With the development of Artificial Intelligence, deep learning models are becoming more popular. CNN models are used for both detection and classification. Self-driving cars must learn to obey road signs in order to drive safely and smoothly. A transfer learning-based approach to traffic sign recognition and classification is used to reduce data costs. This survey analyses different machine learning and deep learning based methods for traffic sign recognition. The traffic sign recognition and classifications are applicable in intelligent transport system, driving autonomous vehicle, conducting road surveys, improving road safety and infrastructure.

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

A vision-based driver assistance system using image processing technology consists of three main modules: road detection, traffic sign detection/identification, obstacle detection. Traffic Sign Recognition is an upcoming research field under applied Computer Vision and Machine Learning which deals with the automatic detection and classification of traffic signs in traffic scene images. In the current paper, we discuss the methodology used to detect the road sign from a traffic sign scene image and present a technique that can be used to construct a system that recognizes road signs in images. Such a system will ensure that each driver is aware of the rules & hazards on the road & will thus help in improving road safety. Also, the location of the road sign in the traffic sign image is unknown. Once these obstacles are overcome, such a system could be integrated in a Driver Assistance System. The primary objective is to develop an algorithm, which will identify various types of road signs from streaming video of real time road scenes in a reasonable time frame. The algorithm has two main parts: the first one, detection which uses the color and shape information to detect a road sign in a traffic scene picture. The second one, prediction & classification based on Neural Network concept. Our algorithm proposes a variety of MATLAB Image Processing Toolbox commands to determine if a road sign is present in current image. If present, the sign is resized and certain features from the Region of Interest (RoI) are fed as a feature vector to a neural net. This trained Neural Classifier then predicts the class of the road sign. Genetic Algorithms (GA) with built-in intelligence could be incorporated into the current system which will try to look for traffic signs only in environments where there is probability of finding/detecting one.

In this paper we describe traffic sign recognition with neural networks in the frequency domain. Traffic signs exist in all countries to regulate the traffic of vehicles and pedestrians. Each country has its own set of traffic signs that are more or less similar. They consist of a set of abstract forms, symbols, numbers and letters, which are combined into different signs. Automatic traffic sign recognition is important for driver assistance systems and for autonomous driving. Traffic sign recognition is a subtype of image recognition. The traffic signs are usually recorded by a camera and must be recognized in real time, i.e. assigned to a class. We use neural networks for traffic sign recognition. The special feature of our method is that the traffic sign recognition does not take place in the spatial domain but in the frequency domain. This has advantages because it is possible to significantly reduce the number of neurons and thus the computing effort of the neural network compared to a conventional neural network.

Traffic sign recognition, including sign detection and classification, is essential for advanced driver assistance systems and autonomous vehicles. This paper introduces a novel machine learning-based sign recognition scheme. In the proposed scheme, detection and classification are realized through learning in a coarse-to-fine manner. Based on the observation that signs in the same category share some common attributes in appearance, the proposed scheme first distinguishes each individual sign category from the background in the coarse learning stage (i.e. sign detection) followed by distinguishing different sign classes within each category in the fine learning stage (i.e. sign classification). Both stages are realized through machine learning techniques. A complete recognition scheme is developed that is effective for simultaneously recognizing multiple categories of traffic signs. In addition, a novel saliency-based feature extraction method is proposed for sign classification. The method segments salient sign regions by leveraging the geodesic energy propagation. Compared with the conventional feature extraction, our method provides more reliable feature extraction from salient sign regions. The proposed scheme is tested and validated on two categories of Chinese traffic signs from Tencent street view. Evaluations on the test dataset show reasonably good performance, with an average of 97.5% true positive and 0.3% false positive on two categories of traffic signs.

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.

To better understand the road condition and make correct driving decisions, traffic sign recognition becomes a crucial component commonly equipped in the vision system of modern autonomous cars. The state-of-the-art traffic sign recognition models are designed with the backbones of deep neural networks (DNNs) since DNNs are powerful to extract more effective visual features that benefit recognition performance. As the recent studies on adversarial attacks have shown that DNNs are easy to be fooled by perturbed images and lead to misclassification, in this article, we explore the vulnerability of the DNN-based traffic sign recognition model. Most existing adversarial attack methods limitedly focus on the white-box attack on the recognition models whose underlying configurations (e.g., network architectures and parameters) are accessible. Differently, we propose a novel attacking method dubbed adaptive square attack (ASA) that can accomplish the black-box attack, i.e., bypassing the access the configurations of the recognition models. Specifically, the proposed ASA method employs an efficient sampling strategy that can generate perturbations for traffic sign images with fewer query times. Extensive experiments on the benchmark data set German traffic sign recognition benchmark with large-scale traffic sign images for autonomous cars show that our proposed ASA method is advanced to perform the black-box attack with high efficiency. Although the generated adversarial traffic sign images by the proposed ASA method are visually similar to the raw images with almost imperceptible differences, they can successfully lead to the misclassification of the state-of-the-art recognition model.

Traffic sign recognition is a pivotal technology in the advancement of autonomous vehicles as it is critical for adhering to country- or region-specific traffic regulations. Defined as an image classification problem in computer vision, traffic sign recognition is a technique that determines the class of a given traffic sign from input data processed by a neural network. Although image classification has been considered a relatively manageable task with the advent of neural networks, traffic sign classification presents its own unique set of challenges due to the similar visual features inherent in traffic signs. This can make designing a softmax-based classifier problematic. To address this challenge, this paper presents a novel traffic sign recognition model that employs angular margin loss. This model optimizes the necessary hyperparameters for the angular margin loss via Bayesian optimization, thereby maximizing the effectiveness of the loss and achieving a high level of classification performance. This paper showcases the impressive performance of the proposed method through experimental results on benchmark datasets for traffic sign classification.

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.

Traffic signs recognition has a crucial role in enhancing the safety and efficienty of autonomous vehicles (AVs). This AVs can contribute to a cleaner and healthier environment by improving fuel efficiency, minimizing travel distances, and deacreasing air pollution. Many artificial intelligence (AI) approaches contribute to develop AVs. Therfore, Convolutional Neural Networks (CNNs) have shown remarkable performance in image classification tasks for AVs, inculding traffic signs recognition. However, training deep CNNs from scratch for traffic sign recognition requires a significant amount of labeled data, which can be time-consuming and ressource-intensive to obtain. Transfer Learning, a technique that leverages pre-trained models on large-scale datasets,offers a promising solution by enabling the transfer of learned feautres from one task to another. This paper presents a comprehensive comparative analysis of three popular transfer learning based CNN approaches, namely ResNet, VGGNet, and MobileNet,for the recognition of traffic signs in the context of AVs.

info:eu-repo/semantics/published

Although supervised approaches for traffic sign classification have demonstrated excellent performance, they are limited to classifying several traffic signs defined in the training dataset. This prevents them from being applied to different domains, i.e., different countries. Herein, we propose a self-supervised approach for few-shot learning-based traffic sign classification. A center-awareness similarity network is designed for the traffic sign problem and trained using an optical flow dataset. Unlike existing supervised traffic sign classification methods, the proposed method does not depend on traffic sign categories defined by the training dataset. It applies to any traffic signs from different countries. We construct a Korean traffic sign classification (KTSC) dataset, including 6000 traffic sign samples and 59 categories. We evaluate the proposed method with baseline methods using the KTSC, German traffic sign, and Belgian traffic sign classification datasets. Experimental results show that the proposed method extends the ability of existing supervised methods and can classify any traffic sign, regardless of region/country dependence. Furthermore, the proposed approach significantly outperforms baseline methods for patch similarity. This approach provides a flexible and robust solution for classifying traffic signs, allowing for accurate categorization of every traffic sign, regardless of regional or national differences.

As an advanced machine vision task, traffic sign recognition is of great significance to the safe driving of autonomous vehicles. Haze has seriously affected the performance of traffic sign recognition. This paper proposes a dehazing network, including multi-scale residual blocks, which significantly affects the recognition of traffic signs in hazy weather. First, we introduce the idea of residual learning, design the end-to-end multi-scale feature information fusion method. Secondly, the study used subjective visual effects and objective evaluation metrics such as Visibility Index (VI) and Realness Index (RI) based on the characteristics of the real-world environment to compare various traditional dehazing and deep learning dehazing method with good performance. Finally, this paper combines image dehazing and traffic sign recognition, using the algorithm of this paper to dehaze the traffic sign images under real-world hazy weather. The experiments show that the algorithm in this paper can improve the performance of traffic sign recognition in hazy weather and fulfil the requirements of real-time image processing. It also proves the effectiveness of the reformulated atmospheric scattering model for the dehazing of traffic sign images.

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