Traffic Sign Recognition System Research Articles

Analysis of Market-Ready Traffic Sign Recognition Systems in Cars: A Test Field Study

Advanced Driver Assistance System (ADAS) represents a collection of vehicle-based intelligent safety systems. One in particular, Traffic Sign Recognition System (TSRS), is designed to detect and interpret roadside information in the form of signage. Even though TSRS has been on the market for more than a decade now, the available ones differ in hardware and software solutions they use, as well as in quantity and typology of signs they recognize. The aim of this study is to determine whether differences between detection and readability accuracy of market-ready TSRS exist and to what extent, as well as how different levels of “graphical changes” on the signs affect their accuracy. For this purpose, signs (“speed limit” and “prohibition of overtaking”) were placed on a test field and 17 vehicles from 14 different car brands underwent testing. Overall, the results showed that sign detection and readability by TSRS differ between car brands and that even small changes in the design of signs can drastically affect TSRS accuracy. Even in a controlled environment where no sign has been altered, there has been a 5% margin of misread signs.

Detecting Change between Urban Road Environments along a Route Based on Static Road Object Occurrences

For over a decade, urban road environment detection has been a target of intensive research. The topic is relevant for the design and implementation of advanced driver assistance systems. Typically, embedded systems are deployed in these for the operation. The environments can be categorized into road environment-types. Abrupt transitions between these pose a traffic safety risk. Road environment-type transitions along a route manifest themselves also in changes in the distribution of traffic signs and other road objects. Can the placement and the detection of traffic signs be modelled jointly with an easy-to-handle stochastic point process, e.g., an inhomogeneous marked Poisson process? Does this model lend itself for real-time application, e.g., via analysis of a log generated by a traffic sign detection and recognition system? How can the chosen change detector help in mitigating the traffic safety risk? A change detection method frequently used for Poisson processes is the cumulative sum (CUSUM) method. Herein, this method is tailored to the specific stochastic model and tested on realistic logs. The use of several change detectors is also considered. Results indicate that a traffic sign-based road environment-type change detection is feasible, though it is not suitable for an immediate intervention.

Traffic Sign Detection for Intelligent Transportation Systems: A Survey

Recently, intelligent transportation systems (ITS) attracts more and more attention for its wide applications. Traffic sign detection and recognition (TSDR) system is an essential task of ITS. It enhances the safety by informing the drivers about the current state of traffic signs and offering valuable information about precautions. This paper reviews the popular traffic sign detection methods (TSD) prevalent in recent literature. The methods are divided into color-based, shape-based, and machine learning based ones. Color space, segmentation method, features, and shape detection method are the terms considered in the review of the detection module. The paper presents a comparison between these methods. Furthermore, a list of publicly available data sets and a discussion on possible future works are provided.

A Lightweight Model for Traffic Sign Classification Based on Enhanced LeNet‐5 Network

For several years, much research has focused on the importance of traffic sign recognition systems, which have played a very important role in road safety. Researchers have exploited the techniques of machine learning, deep learning, and image processing to carry out their research successfully. The new and recent research on road sign classification and recognition systems is the result of the use of deep learning‐based architectures such as the convolutional neural network (CNN) architectures. In this research work, the goal was to achieve a CNN model that is lightweight and easily implemented for an embedded application and with excellent classification accuracy. We choose to work with an improved network LeNet‐5 model for the classification of road signs. We trained our model network on the German Traffic Sign Recognition Benchmark (GTSRB) database and also on the Belgian Traffic Sign Data Set (BTSD), and it gave good results compared to other models tested by us and others tested by different researchers. The accuracy was 99.84% on GTSRB and 98.37% on BTSD. The lightness and the reduced number of parameters of our model (0.38 million) based on the enhanced LeNet‐5 network pushed us to test our model for an embedded application using a webcam. The results we found are efficient, which emphasize the effectiveness of our method.

Real-time embedded TSR using MDEffNet: An efficient compact DNN on varied multi-resolution images across multiple databases

Deep Learning in Traffic Sign Recognition (TSR) System is rapidly developing. Still, not much work has been reported in the literature on architectures that can work efficiently on multiple (more than two) databases across different countries. Each of these databases incorporates images of varied resolutions. Here, a Deep Neural Network called Multiple Database Efficient Network (MDEffNet) has been proposed for TSR. Batch normalization layers have been introduced to reduce the internal co-variant shift, and further, data augmentation has been carried out to achieve high efficiency with reasonable training time. Results at each step of the proposed architecture have been provided. The proposed model is compared with some state-of-the-art methods based on single or two databases, and solo works done in multiple (three) databases originating from different countries. The comparisons prove the high effectiveness of the proposed model. The achieved efficiency is much higher than all of them, including the average human ability. Deep learning techniques often suffer from increased memory and computational requirements. MDEffNet has only 1.36 million parameters ([Formula: see text] 26 times lower than the state-of-art methods) with 130 s per epoch, which is relatively low. Hence, it is also suitable for real-time embedded TSR systems.

Real-time method for traffic sign detection and recognition based on YOLOv3-tiny with multiscale feature extraction

As a part of Intelligent Transportation System (ITS), the vehicle traffic sign detection and recognition system have been paid more attention by Intelligent transportation researchers, the traffic sign detection and recognition algorithm based on convolution neural network has great advantages in expansibility and robustness, but it still has great optimization space inaccuracy, computation and storage space. In this paper, we design a multiscale feature fusion algorithm for traffic sign detection and recognition. In order to improve the accuracy of the network, the gaussian distribution characteristics are used in the loss function. The training and analysis of two neural networks with different feature scales and YOLOv3-tiny were carried out on the Tsinghua-Tencent open traffic sign dataset. The experimental results show that the detection and recognition of the targets by networks with multiple feature scales have improved significantly, and the recall and accuracy are 95.32% and 93.13% respectively. Finally, the algorithm of traffic sign detection and recognition is verified on the NVIDIA Jetson Tx2 platform and delivers 28 fps outstanding performances.

An automated system for traffic sign recognition using convolutional neural network

TSR (Traffic Sign Recognition) represents an important feature of advanced driver assistance system, contributing to the safety of the drivers, autonomous vehicles as well and to increase driving comfort. In today’s world road conditions drastically improved as compared with past decades. Obviously, vehicle’s speed increased. So, on driver’s point of view there might be chances of neglecting mandatory road signs while driving. This paper explores the system to helps the driver about recognition of road signs to avoid road accidents. TSR is challenging task, while its accuracy depends on two aspects: feature extractor and classifier. Current popular algorithms mainly deploy CNN (Convolutional Neural Network) to execute both feature extraction and classification. In this paper, we implement the traffic sign recognition by using CNN, the CNN will be trained by using the dataset of 43 different classes of traffic signs along with TensorFlow library. The results will show the 95% accuracy.

Road Traffic Sign Recognition and Vehicle Accident Avoidance System

Traffic sign recognition and vehicle accident avoidance system gets a of interest late by huge scale organizations, e.g., Apple, Google and Volkswagen and so on driven by the market requirements for smart applications, e.g. Automatic Driving and Driver Assistance Systems , Mobile Eye, Mobile Mapping and many more.In this paper, traffic sign recognition and vehicle accident avoidance system is utilized to keep up traffic and maintain a strategic distance from vehicle, caution the occupied drivers, and avoid activities that can lead a vehicle. An on-going programmed sign recognition and detection can support the driver with safety. System propose automated real time system which will capture the traffic sign and show it at driver dashboard with front obstacle exact distance on screen. The PiCam is associated with Raspberry Pi and it is utilized to capture pictures .Screen is utilized to show the system output e.g. appearing of traffic sign and separation of vehicle. This framework is configuration to maintain a strategic distance from vehicle happening on street.

Traffic sign detection and recognition by improving Region of Interest (ROI) division to support driver assistance system

Traffic signs detection and recognition is a technology that allows automatic identification of traffic signs. In the implementation, it mostly uses a camera to capture video or real-time conditions. The Traffic detection and recognition system can give benefits to improve safety for drivers or passengers. Therefore, traffic signs detection and recognition system are one of the important parts in Driver Assistance Systems (DAS), besides that it is also used in video surveillance to improve traffic safety in the field of Intelligent Transportation System (ITS). In this paper, we propose a method for detecting and recognizing traffic signs by combining color and shape detection and divide the interest object to several regions for object recognition. The first step is image transformation, which aims to determine interest objects in the camera view. Such a process was using hue, saturation, value (HSV) color space, grey level, and black and white (BW) level. The next step is the filter and the segmentation process. The result of the filter and segmentation process then detects area objects by using region determination. The last step is the region of interest (ROI), which is used to recognizing sign traffic by the sum of absolute differences (SAD), and we try to improve area selection of ROI around nine parts. The results of the experiment proved that the proposed method was relatively good with the success rate for detecting objects about 97,38 %, and the true positive of recognition result about 81,67 %.

Deep Learning in Autonomous Vehicles: A Review

Autonomous vehicles (AVs) represent a transformative technology that has the potential to revolutionize transportation systems worldwide. Deep learning, a subset of machine learning, plays a crucial role in the development and operation of AVs by enabling perception, decision-making, and control. This paper provides a comprehensive review of the role of deep learning in AVs, covering topics such as object detection, lane detection, traffic sign recognition, localization, mapping, behaviour planning, and control systems. The paper also discusses the challenges and limitations of deep learning in AVs, including data quality and quantity, safety and security concerns, environmental factors, and regulatory and legal challenges. Furthermore, the paper examines recent advancements in deep learning models for AVs, integration with other technologies, potential impacts on society and industry, and future research directions. By providing a detailed overview of deep learning applications in AVs, this review aims to contribute to the ongoing discourse surrounding the development and deployment of autonomous vehicles.

Performance enhancement techniques for traffic sign recognition using a deep neural network

An advanced traffic sign recognition (ATSR) system using novel pre-processing techniques and optimization techniques has been proposed. During the pre-processing of input road images, color contrasts are enhanced and edges are made clearer, for easier detection of small-sized traffic signs. YOLOv3 has been modified to build our traffic sign detector, since it is an efficient and effective deep neural network. In this YOLOv3 modifications, grid optimization and anchor box optimization were done to optimize the detection performance on small-sized traffic signs. We trained the system on our traffic sign dataset and tested the recognition performance using the Mean Average Precision (MAP) on the Korean Traffic Sign Dataset (KTSD) and German Traffic Sign Detection Benchmark (GTSDB). We used the bisection method for selecting the optimum threshold of confidence score to reduce false predictions. Our ATSR system is capable of recognizing Prohibitory, Mandatory, and Danger class traffic signs from road images. ATSR can detect small-sized traffic signs accurately along with big-sized traffic signs. It shows the best recognition performance of 98.15% on the challenging KTSD (the previously reported best performance was 90.07%) and 100% on the GTSDB. Result comparisons show that ATSR significantly outperforms ITSR, TS detector, YOLOv3, and D-patches, on KTSD.

Poisoning and Evasion Attacks Against Deep Learning Algorithms in Autonomous Vehicles

With the ongoing development and improvement of deep learning technology, autonomous vehicles (AVs) have made tremendous progress in recent years. Despite its great potential, AV supported by deep learning technology still faces numerous security threats, which prevent AV from being putting into large-scale practice. Aiming at this challenging situation, in this paper, we would like to exploit two attacks against deep learning algorithms in traffic sign recognition system by leveraging particle swarm optimization. Specifically, we first exploit the PAPSO (poisoning attack with particle swarm optimization) which focuses on the training process of the deep learning algorithms in the traffic sign recognition system, i.e., the attacker injects crafted samples into the training dataset, causing a reduction in classification accuracy of the traffic sign recognition system. Then, we also explore the EAPSO (evasion attack with particle swarm optimization) which on the other hand focuses on the interference process of the deep learning algorithms, i.e., the attacker adds some hardly perceptible perturbations to the targeted test sample, leading to a misclassification on it. Extensive experiments are conducted to shed light on the effectiveness of our attacks, and some corresponding defense strategies are also presented.

Recognizing New Classes with Synthetic Data in the Loop: Application to Traffic Sign Recognition.

On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio for new/known classes; even for more challenging ratios such as , the results are also very positive.

Real-Time Traffic Sign Detection and Recognition System for Assistive Driving

Real-Time Traffic Sign Detection and Recognition System for Assistive Driving

A Survey of the Inadequacies in Traffic Sign Recognition Systems for Autonomous Vehicles

A Survey of the Inadequacies in Traffic Sign Recognition Systems for Autonomous Vehicles

Transfer Learning Based Traffic Sign Recognition System for Surveillance Application

Road Traffic Recognition is very important in many applications, such as automated deployment, traffic mapping, and vehicle tracking. Proposed traffic sign recognition system tails the transfer learning method that is frequently used in neural network uses. The benefit of expending this technique is that the initially network has been trained with a rich set of features appropriate to a wide range of images. Once the network is trained , learning can be transferred to the new activity adjustment to the network. Firsthand Indian traffic sign dataset is used.New results exhibit that the suggested method can accomplish modest outcomes when matched with other related techniques.

Lightweight deep network for traffic sign classification

Deeper neural networks have achieved great results in the field of computer vision and have been successfully applied to tasks such as traffic sign recognition. However, as traffic sign recognition systems are often deployed in resource-constrained environments, it is critical for the network design to be slim and accurate in these instances. Accordingly, in this paper, we propose two novel lightweight networks that can obtain higher recognition precision while preserving less trainable parameters in the models. Knowledge distillation transfers the knowledge in a trained model, called the teacher network, to a smaller model, called the student network. Moreover, to improve the accuracy of traffic sign recognition, we also implement a new module in our teacher network that combines two streams of feature channels with dense connectivity. To enable easy deployment on mobile devices, our student network is a simple end-to-end architecture containing five convolutional layers and a fully connected layer. Furthermore, by referring to the values of batch normalization (BN) scaling factors towards zero to identify insignificant channels, we prune redundant channels from the student network, yielding a compact model with accuracy comparable to that of more complex models. Our teacher network exhibited an accuracy rate of 93.16% when trained and tested on the CIFAR-10 general dataset. Using the knowledge of our teacher network, we train the student network on the GTSRB and BTSC traffic sign datasets. Thus, our student model uses only 0.8 million parameters while still achieving accuracy of 99.61% and 99.13% respectively on both datasets. All experimental results show that our lightweight networks can be useful when deploying deep convolutional neural networks (CNNs) on mobile embedded devices.

Active Safety with Traffic Sign Recognition System in Vehicular Ad-Hoc Network (Vanet) using Classifier Techniques

Intelligent Transportation System (ITS) is one of the major research areas where the researchers focus on Vehicle traffic prediction, efficient routing algorithms, security in vehicle communication, various QoS parameters and so on..Wireless technology is progressing faster with time. People are doing research these days generally in the field of Wireless communication. VANET is the most developing exploration territory in remote correspondence. With the headway and development of the VANET, there will be an incredible upheaval in the field of telecommunication regarding quick handovers, arrange accessibility, security, wellbeing with the utilization of cutting edge applications and so on. VANET innovation is progressing with the progression of time however there are numerous issues that must be routed to make the system more overwhelming. In perspective of aforementioned, in this paper we have proposed an effective mechanism for object detection in highways using Neural Networking and proving driver assistance to enhance the existing system to the next level as Intelligent Transportation System.

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges.

The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system.

Traffic sign detection method based on Faster R-CNN

Traffic sign detection is the pivotal technology of the traffic sign recognition system. In this article, a traffic sign detection method comes up based on Faster R-CNN deep learning framework. In this method, a convolution neural network is devoted to extract traffic sign image features automatically, and the extracted convolution feature map is sent into a Region Proposal Network (RPN) for foreground objects filtration and regression of bounding boxes. Then the proposed regions are mapped to the feature map, and the fixed-size proposal boxes via Region of Interest pooling layer (RoI). After that, we use the classification network to perform specific classification tasks and further compute the bounding box regression. The experiments performs on the German Traffic Sign Detection Benchmark (GTSDB) and the experimental results show that the method has effectiveness and robustness to different light, block, and motion.