Traffic Sign Detection System Research Articles

Real-Time Navigation Roads: Lightweight and Efficient Convolutional Neural Network (LE-CNN) for Arabic Traffic Sign Recognition in Intelligent Transportation Systems (ITS)

Smart cities are now embracing the new frontier of urban living, with advanced technology being used to enhance the quality of life for residents. Many of these cities have developed transportation systems that improve efficiency and sustainability, as well as quality. Integrating cutting-edge transportation technology and data-driven solutions improves safety, reduces environmental impact, optimizes traffic flow during peak hours, and reduces congestion. Intelligent transportation systems consist of many systems, one of which is traffic sign detection. This type of system utilizes many advanced techniques and technologies, such as machine learning and computer vision techniques. A variety of traffic signs, such as yield signs, stop signs, speed limits, and pedestrian crossings, are among those that the traffic sign detection system is trained to recognize and interpret. Ensuring accurate and robust traffic sign recognition is paramount for the safe deployment of self-driving cars in diverse and challenging environments like the Arab world. However, existing methods often face many challenges, such as variability in the appearance of signs, real-time processing, occlusions that can block signs, low-quality images, and others. This paper introduces an advanced Lightweight and Efficient Convolutional Neural Network (LE-CNN) architecture specifically designed for accurate and real-time Arabic traffic sign classification. The proposed LE-CNN architecture leverages the efficacy of depth-wise separable convolutions and channel pruning to achieve significant performance improvements in both speed and accuracy compared to existing models. An extensive evaluation of the LE-CNN on the Arabic traffic sign dataset that was carried out demonstrates an impressive accuracy of 96.5% while maintaining superior performance with a remarkably low inference time of 1.65 s, crucial for real-time applications in self-driving cars. It achieves high accuracy with low false positive and false negative rates, demonstrating its potential for real-world applications like autonomous driving and advanced driver-assistance systems.

Traffic Sign Detection and Recognition Using YOLO Object Detection Algorithm: A Systematic Review

Context: YOLO (You Look Only Once) is an algorithm based on deep neural networks with real-time object detection capabilities. This state-of-the-art technology is widely available, mainly due to its speed and precision. Since its conception, YOLO has been applied to detect and recognize traffic signs, pedestrians, traffic lights, vehicles, and so on. Objective: The goal of this research is to systematically analyze the YOLO object detection algorithm, applied to traffic sign detection and recognition systems, from five relevant aspects of this technology: applications, datasets, metrics, hardware, and challenges. Method: This study performs a systematic literature review (SLR) of studies on traffic sign detection and recognition using YOLO published in the years 2016–2022. Results: The search found 115 primary studies relevant to the goal of this research. After analyzing these investigations, the following relevant results were obtained. The most common applications of YOLO in this field are vehicular security and intelligent and autonomous vehicles. The majority of the sign datasets used to train, test, and validate YOLO-based systems are publicly available, with an emphasis on datasets from Germany and China. It has also been discovered that most works present sophisticated detection, classification, and processing speed metrics for traffic sign detection and recognition systems by using the different versions of YOLO. In addition, the most popular desktop data processing hardwares are Nvidia RTX 2080 and Titan Tesla V100 and, in the case of embedded or mobile GPU platforms, Jetson Xavier NX. Finally, seven relevant challenges that these systems face when operating in real road conditions have been identified. With this in mind, research has been reclassified to address these challenges in each case. Conclusions: This SLR is the most relevant and current work in the field of technology development applied to the detection and recognition of traffic signs using YOLO. In addition, insights are provided about future work that could be conducted to improve the field.

Application of Sustainable Blockchain Technology in the Internet of Vehicles: Innovation in Traffic Sign Detection Systems

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.

Traffic Sign Detection and Recognition System for Autonomous Vehicle

Abstract: Traffic Sign Recognition and detection system is specifically useful for drivers and driver-less cars. By identifying traffic signs accurately and effectively it improves the driving safety of autonomous cars. This research paper presents a comprehensive approach for the development of a traffic sign detection system using Convolution Neural Network, TensorFlow and OpenCV to classify the traffic signs in real-time effectively. TensorFlow and OpenCV play an important role in shaping effective traffic detection and recognition system. We have explained the process of data collection, preparation, model architecture and the integration of TensorFlow for training and interference. OpenCV for image processing and real time feed processing, ensuring seamless implementation on various hardware platform. The model uses German Traffic Sign Recognition dataset. The results show that the proposed system achieves high accuracy in detecting and recognizing traffic sign making it a valuable system for both autonomous vehicles and human drivers

Real-Time Traffic Sign Detection: A Case Study in a Santa Clara Suburban Neighborhood

This research project aims to develop a real-time traffic sign detection system using the YOLOv5 architecture and deploys it for efficient traffic sign recognition during a drive in a suburban neighborhood. The project's primary objectives are to train the YOLOv5 model on a diverse dataset of traffic sign images and deploy the model on a suitable hardware platform capable of real-time inference. The project will involve collecting a comprehensive dataset of traffic sign images. By leveraging the trained YOLOv5 model, the system will detect and classify traffic signs from a real-time camera on a dashboard inside a vehicle. The performance of the deployed system will be evaluated based on its accuracy in detecting traffic signs, real-time processing speed, and overall reliability. During a case study in a suburban neighborhood, the system demonstrated a notable 96% accuracy in detecting traffic signs. This research's findings have the potential to improve road safety and traffic management by providing timely and accurate real-time information about traffic signs and can pave the way for further research into autonomous driving.

Using LBP and HOG, A Traffic Sign Detection and Recognition System

Using LBP and HOG, A Traffic Sign Detection and Recognition System

Mobile Robot Navigation Based on Embedded Computer Vision

The current computational advance allows the development of technological solutions using tools, such as mobile robots and programmable electronic systems. We present a design that integrates the Khepera IV mobile robot with an NVIDIA Jetson Xavier NX board. This system executes an algorithm for navigation control based on computer vision and the use of a model for object detection. Among the functionalities that this integration adds to the Khepera IV in generating guided driving are trajectory tracking for safe navigation and the detection of traffic signs for decision-making. We built a robotic platform to test the system in real time. We also compared it with a digital model of the Khepera IV in the CoppeliaSim simulator. The navigation control results show significant improvements over previous works. This is evident in both the maximum navigation speed and the hit rate of the traffic sign detection system. We also analyzed the navigation control, which achieved an average success rate of 93%. The architecture allows testing new control techniques or algorithms based on Python, facilitating future improvements.

Traffic Sign Detection and Voice Alert

Abstract: The Traffic Sign Detection and Voice Alert is an intelligent system designed to improve road safety by detecting traffic signs andproviding voice alerts to the driver. The project is based on computer vision and machine learning techniques, and it uses a camera to capture real-time images of the road and traffic signs. The system analyzes the images to detect traffic signs, such as speed limit signs, stop signs, and no entry signs, using deep learning algorithms. Once a traffic sign is detected, the system provides a voice alert to the driver, informing them of the sign's message. The system has several advantages over traditional traffic sign detection systems. Firstly, it is more accurate and reliable due to the use of deep learning algorithms. Secondly, it provides real-time alerts to the driver, which helps them to stay focused on the road and avoid accidents. Finally, it is cost-effective and can be easily installed in any vehicle.

REAL-TIME TRAFFIC SIGN DETECTION AND MAPPING FOR DRIVER ASSISTANCE USING CNN AND OPEN-SOURCE LIBRARIES

The rising number of accidents caused by failure to obey traffic laws and road signs can be addressed through a traffic sign detection system and its mapping for road sign navigation. This paper addresses a solution that utilizes generated training data made from photos and location of road traffic signs. Our system detects and notifies drivers of upcoming traffic signs during navigation, even if they are obscured by weather or other factors. The system assures fewer accidents and allows drivers to focus on driving rather than reading every single traffic sign on road and achieves the accuracy up to 99.27%.

Equirectangular Image Data Detection, Segmentation and Classification of Varying Sized Traffic Signs: A Comparison of Deep Learning Methods.

There are known limitations in mobile omnidirectional camera systems with an equirectangular projection in the wild, such as momentum-caused object distortion within images, partial occlusion and the effects of environmental settings. The localization, instance segmentation and classification of traffic signs from image data is of significant importance to applications such as Traffic Sign Detection and Recognition (TSDR) and Advanced Driver Assistance Systems (ADAS). Works show the efficacy of using state-of-the-art deep pixel-wise methods for this task yet rely on the input of classical landscape image data, automatic camera focus and collection in ideal weather settings, which does not accurately represent the application of technologies in the wild. We present a new processing pipeline for extracting objects within omnidirectional images in the wild, with included demonstration in a Traffic Sign Detection and Recognition (TDSR) system. We compare Mask RCNN, Cascade RCNN, and Hybrid Task Cascade (HTC) methods, while testing RsNeXt 101, Swin-S and HRNetV2p backbones, with transfer learning for localization and instance segmentation. The results from our multinomial classification experiment show that using our proposed pipeline, given that a traffic sign is detected, there is above a 95% chance that it is classified correctly between 12 classes despite the limitations mentioned. Our results on the projected images should provide a path to use omnidirectional images with image processing to enable the full surrounding awareness from one image source.

Noise Removal in Traffic Sign Detection Systems

Noise Removal in Traffic Sign Detection Systems

Substation Danger Sign Detection and Recognition using Convolutional Neural Networks

This paper focuses on the training of a deep neural network regarding danger sign detection and recognition in a substation. It involved applying the concepts of neural networks and computer vision to achieve results similar to traffic sign and number plate detection systems. The input data were captured in three distinct formats, i.e. grayscale, RGB, and YCbCr, which have been used as a base for comparison in this paper. The efficiency of the neural network was tested on a unique data set involving danger signs present in industrial and processing facilities. The data set was unique, consisting of four distinct symbols. The trained data were selected so that they would not facilitate overfitting and also would not be under fitted. The accuracy of the model varied with the input type and was tested with two distinct classifiers, CNN and SVM, and the results were compared. The model was designed to be fast and accurate, and it can be implemented on mobile devices.

Automatic Detection and Categorization of Road Traffic Signs using a Knowledge-Assisted Method

Deep Learning is proving to be a boon for the latest smart city applications. For efficient mobility-based applications, Artificial Intelligence (AI)-based Intelligent Transportation Systems (ITS) are indispensable. In an autonomous driving scenario, a Traffic Sign Detection and Recognition system (TSDR) helps the drivers. Eventually, with its proper use, the number of accidents on the road can also be reduced. TSDR is an important technique in ITS but it can be hard to implement because of the mess, different levels of light, different sign sizes, and changing weather. In the past, different object detection models such as Fast regions with convolutional neural networks (RCNN) and Faster R-CNN were utilised for this purpose but due to the sluggish pace of detection, these models are inefficient. In this article, a state-of-the-art approach using ”You Only Look Once” version 4 (YOLOv4) is being used for the correct detection and recognition of traffic signs. YOLOv4 utilizes convolutional neural networks (CNN) for the instant detection of objects. YOLO framework processes input image in a single instance and it does not require a complex detection pipeline, making it extremely fast. Therefore, the proposed approach is feasible for real-time traffic sign detection and recognition. The detection accuracy of the proposed YOLOv4 model is improved by adjusting the relevant parameters and retraining the model on manually annotated data set. It has been demonstrated in results that the performance of proposed model is adequate.

DFR-TSD: A Sustainable Deep Learning Based Framework for Sustainable Robust Traffic Sign Detection under Challenging Weather Conditions

The development of reliable and sustainable traffic sign detection under difficult weather conditions, or DFR-TSD, is a key step in creating effective, safe, and sustainable autonomous driving systems. The suggested sustainable framework makes use of deep learning techniques to overcome the drawbacks of the current traffic sign detection systems, especially in difficult weather circumstances like haze and snow. The system uses a sustainable CNN pre-processing step to make traffic signs more visible in photos that have been impacted by the weather, followed by a sustainable pre-trained ResNet-50 model to recognize traffic signs. On the CURE-TSD dataset, which includes difficult weather circumstances such as haze, snow, and fog, the suggested sustainable framework was assessed. The testing findings showed how sustainably well the suggested framework performed in identifying traffic signs in adverse weather. The suggested sustainable framework outperforms previous approaches with a sustainable accuracy rating of 98.83%. The outcomes show that sustainable deep learning methods have the potential to enhance traffic sign identification models' functionality. The proposed sustainable framework’s front end offers a user-friendly interface that enables users to upload test photographs and view the results of the detection. There are four sustainable buttons on the UI for loading the model, uploading test photographs, spotting signs, and seeing the training graph. The Tkinter framework, which offers a user-friendly GUI toolkit that enables developers to quickly design and customize sustainable GUI programs, is used to develop the front end. The suggested sustainable DFR-TSD framework is a potential sustainable option for reliable traffic sign detection in adverse weather due to the sustainable pre-processing step, the sustainable pre-trained ResNet-50 model, and the sustainable user-friendly interface.

TRAFFIC SIGN RECOGNITION WITH CONVOLUTIONAL NEURAL NETWORK

Recognizing road signs is one of the most important steps drivers can take to help prevent accidents. The purpose of the research work is to develop a recognition system, increasing the classification accuracy of the model, using deep learning methods of the road sign recognition system for drivers in real time on the road. Stages of road sign image classification were carried out, and other authors' solutions were analyzed. In addition, in this work, a convolutional neural network (CNN) was used for an autonomous traffic and road sign detection and recognition system. The proposed system works in real-time on the recognition of road signs images. In this paper, a model is trained using deep learning of 43 different road signs using existing datasets and collected local road signs. A traffic sign detection and recognition system is presented using an 8-layer convolutional neural network, which acquires different functions by training different types of traffic signs.
 In previous studies, models were trained using simple machine learning algorithms, but the relevance of this study is that a CNN model was trained for a classification task based on convolutional neural networks using deep learning. As a result of the study, classification accuracy of 95% was obtained using deep learning methods. As a novelty of the work, it is possible to note the diversity of the convolutional network methods used to increase the efficiency of the used data set and model training algorithms, the variety of received road signs and algorithms for its recognition, as well as the achievement of a high accuracy rate. This allowed the system to overcome the limited accuracy and performance issues caused by environmental factors, and to be more versatile and accurate than most modern systems.

Noise Removal in Traffic Sign Detection Systems

The application of Traffic sign detection and recognition is growing in traffic assistant driving systems and automatic driving systems. It helps drivers and automatic driving systems to detect and recognize the traffic signs effectively. However, it is found that it may be difficult for these systems to work in challenging environments like rain, haze, hue, etc. To help the detection systems to have better performance in challenging conditions like rain and haze, we propose the use of a deep learning technique based on a Convolutional Neural Network to process visual data. The processed data could be used in the detection. We are using the NoiseNet model [11], a noise reduction network for our architecture. The model is trained to enhance images in patches instead of as a whole. The training is done using the Challenging Unreal and Real Environment - Traffic Sign Detection Dataset(CURE-TSD) which contains videos of different roads in various challenging situations. The enhanced images obtained are compared using the object detection algorithms YOLO and Faster RCNN. The Mean Absolute Error(MAE) of original and enhanced images are calculated and compared for two classes of images - rain and haze for both the algorithms. The proposed approach achieved an average Peak Signal to Noise Ration(PSNR) of 25.30 and an Structural Similarity(SSIM) of 0.88. The average MAE values of YOLO and Faster RCNN model reduced by 0.11 and 0.30 respectively on using enhanced images.

Sensing and Detection of Traffic Signs Using CNNs: An Assessment on Their Performance.

Traffic sign detection systems constitute a key component in trending real-world applications such as autonomous driving and driver safety and assistance. In recent years, many learning systems have been used to help detect traffic signs more accurately, such as ResNet, Vgg, Squeeznet, and DenseNet, but which of these systems can perform better than the others is debatable. They must be examined carefully and under the same conditions. To check the system under the same conditions, you must first have the same database structure. Moreover, the practice of training under the same number of epochs should be the same. Other points to consider are the language in which the coding operation was performed as well as the method of calling the training system, which should be the same. As a result, under these conditions, it can be said that the comparison between different education systems has been done under equal conditions, and the result of this analogy will be valid. In this article, traffic sign detection was done using AlexNet and XresNet 50 training methods, which had not been used until now. Then, with the implementation of ResNet 18, 34, and 50, DenseNet 121, 169, and 201, Vgg 16_bn and Vgg19_bn, AlexNet, SqueezeNet1_0, and SqueezeNet1_1 training methods under completely the same conditions. The results are compared with each other, and finally, the best ones for use in detecting traffic signs are introduced. The experimental results showed that, considering parameters train loss, valid loss, accuracy, error rate and Time, three types of CNN learning models Vgg 16_bn, Vgg19_bn and, AlexNet performed better for the intended purpose. As a result, these three types of learning models can be considered for further studies.

Research on Detection and Recognition of Traffic Signs Based on Convolutional Neural Networks

The road traffic sign detection and identification (TSDR) system is a subsystem of the advanced driver assistance system. It has received extensive attention from domestic and foreign researchers. The TSDR system is mainly composed of two parts: the detection of traffic signs and the identification and output of traffic signs. This paper focuses on the research of TSDR system and proposes a method based on faster R-CNN combined with part of the low-level feature map. The lightweight model MobileNet is used to finish the traffic sign detection. Experiments show that this method has a certain effect on the detection of small-scale traffic signs, and the detection speed is fast. The LeNet5 and Inception V3 network model are used for traffic sign recognition. The model is optimized by adjusting parameters such as the learning rate and batch size. It shows that for LeNet5, when the learning rate = 0.001, the recognition rate can reach 92.5%. For the Inception V3 model, when the learning rate is 0.005, it has a higher recognition rate than the LeNet5 model by 4.9 percentage points.

Literature Review on Vision-based Traffic Sign Detection and Recognition using Artificial Intelligence

Abstract: With the expansion in the number of vehicles on the road, the development of technology, and other factors, safety has elevated to the top of automotive industries. Following traffic laws and regulations are essential in Advanced Driver Assistance Systems (ADAS) to prevent collisions. Over the past few decades, traffic sign recognition has gained significant interest due to its quick processing speed and the reduction of human interactions, which lowers risk with the assistance of Artificial Intelligence. While trying to focus on the road, drivers sometimes miss signage on the side of the road, which could be vulnerable for both them and the pedestrians. If the driver could be alerted effectively without having to change their focus, this issue may be avoided. When a sign is detected and recognised, the Traffic Sign Detection and Recognition (TSDR) system alerts the driver to any approaching signs. In addition to ensuring road safety, this also gives drivers a little more comfort while negotiating challenging or unfamiliar roads. This paper provides a comprehensive survey on traffic sign detection and recognition with details of algorithms, architectures, and methods implemented. Based on the investigation of several experts, this literature study was completed and may be used as a future reference

Evaluation of focal loss based deep neural networks for traffic sign detection

ABSTRACT With advancements in autonomous driving, demand for stringent and computationally efficient traffic sign detection systems has increased. However, bringing such a system to a deployable level requires handling critical accuracy and processing speed issues. A focal loss-based single-stage object detector, i.e RetinaNet, is used as a trade-off between accuracy and processing speed as it handles the class imbalance problem of the single-stage detector and is thus suitable for traffic sign detection (TSD). We assessed the detector’s performance by combining various feature extractors such as ResNet-50, ResNet-101, and ResNet-152 on three publicly available TSD benchmark datasets. Performance comparison of the detector using different backbone includes evaluation parameters like mean average precision (mAP), memory allocation, running time, and floating-point operations. From the evaluation results, we found that the RetinaNet object detector using the ResNet-152 backbone obtains the best mAP, while that using ResNet-101 strikes the best trade-off between accuracy and execution time. The motivation behind benchmarking the detector on different datasets is to analyse the detector’s performance on different TSD benchmark datasets. Among the three feature extractors, the RetinaNet model trained using the ResNet-50 backbone is an excellent model in memory consumption, making it an optimal choice for low-cost embedded devices deployment.