Traffic Detection System Research Articles

Noise Removal in Traffic Sign Detection Systems

Noise Removal in Traffic Sign Detection Systems

Traffic Signal Violation Detection System

Traffic Signal Violation Detection System

Real-Time Tunnel Abnormal Sound Detection Algorithm Using Convolutional Neural Networks

In the traffic industry, the automatic accident detection system is a major concern. Although image-based and radar-based traffic accident detection systems are commonly employed, they have several drawbacks, including the need to secure the camera’s field of view, a high rate of false alarms, and a lengthy detection time. Using a real-time acoustic surveillance system and the classification algorithm via Convolutional Neural Network (CNN), this article proposes several methods for identifying abnormal situations, such as a car crash or tire skid sound, to overcome the limitations of existing methods. We create an audio database by collecting sounds from two tunnels in South Korea using self-made microphones for eight months and classifying them into three categories: car crash, tire skid, and normal environmental sounds. We establish a three-step classification procedure using an algorithm. We compare the detection rate and false alarm rate of our proposed method to those of deep learning techniques including MLP (Multi-Layer Perceptron), Long-Short Term Memory, ShuffleNetv2, and MobileNetv2. In addition, we present a method for filtering out irrelevant sound data to improve the computational efficiency of our approach.

Frequency Domain Feature Based Robust Malicious Traffic Detection

Machine learning (ML) based malicious traffic detection is an emerging security paradigm, particularly for zero-day attack detection, which is complementary to existing rule based detection. However, the existing ML based detection achieves low detection accuracy and low throughput incurred by inefficient traffic features extraction. Thus, they cannot detect attacks in realtime, especially in high throughput networks. Particularly, these detection systems similar to the existing rule based detection can be easily evaded by sophisticated attacks. To this end, we propose Whisper, a realtime ML based malicious traffic detection system that achieves both high accuracy and high throughput by utilizing frequency domain features. It utilizes sequential information represented by the frequency domain features to achieve bounded information loss, which ensures high detection accuracy, and meanwhile constrains the scale of features to achieve high detection throughput. In particular, attackers cannot easily interfere with the frequency domain features and thus Whisper is robust against various evasion attacks. Our experiments with 74 types of attacks demonstrate that, compared with the state-of-the-art systems, Whisper can accurately detect various sophisticated and stealthy attacks, achieving at most 18.36% improvement of AUC, while achieving two orders of magnitude throughput. Even under various evasion attacks, Whisper is still able to maintain around 90% detection accuracy.

Image-based automatic traffic lights detection system for autonomous cars: a review

Image-based automatic traffic lights detection system for autonomous cars: a review

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.

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.

Traffic Object Detection and Recognition Based on the Attentional Visual Field of Drivers

Traffic object detection and recognition systems play an essential role in Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AV). In this research, we focus on four important classes of traffic objects: traffic signs, road vehicles, pedestrians, and traffic lights. We first review the major traditional machine learning and deep learning methods that have been used in the literature to detect and recognize these objects. We provide a vision-based framework that detects and recognizes traffic objects inside and outside the attentional visual area of drivers. This approach uses the driver 3D absolute coordinates of the gaze point obtained by the combined, cross-calibrated use of a front-view stereo imaging system and a non-contact 3D gaze tracker. A combination of multi-scale HOG-SVM and Faster R-CNN-based models are utilized in the detection stage. The recognition stage is performed with a ResNet-101 network to verify sets of generated hypotheses. We applied our approach on real data collected during drives in an urban environment with the RoadLAB instrumented vehicle. Our framework achieved 91% of correct object detections and provided promising results in the object recognition stage.

An optimised Darknet traffic detection system using modified locally connected CNN - BiLSTM network

An optimised Darknet traffic detection system using modified locally connected CNN - BiLSTM network

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.

Prevention of criminal offences in passenger road transport in EU countries

Effective prevention of criminal offences in passenger road transport includes the development and implementation of safer infrastructure and modern modification of the safety features of vehicles. It is important to adopt and enforce legal initiatives to reduce the main risks that entail serious traffic accidents. One of the main priorities of appropriate prevention is to improve legal education of the public. The aim of the article was to consider the current state of prevention of criminal offences in passenger road transport on the territory of Europe. Observation and comparative law were the main methodological tools. The conducted research showed that the EU states apply a variety of approaches to the prevention of criminal offences in automobile passenger transport, such as Vision Zero and the systemic approach Safe System with its Sustainable Safety sub-topic. The need to adopt the experience of Sweden and the Netherlands, which have achieved impressive road safety indicators, in Ukraine seems justified. The eCall traffic accident detection and prevention system and the ESRA innovative prevention method can be examples for implementation in Ukraine. Belgium’s school education program Road Safety Weeks is aimed at minimizing teenage drunk driving, speeding, driver fatigue and distraction. The idea of the programme can be implemented into the relevant preventive activities in Ukraine. A promising vector of further scientific research will be a comparative analysis of the implementation of crime prevention on passenger road transport in the EU countries.

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.

A video processing algorithm using temporal intuitionistic fuzzy sets

Video Processing has found enormous applications in recent times from security systems to interplanetary missions. In real-life situations, most of the videos are fuzzy/vague/uncertain. Intuitionistic fuzzy set (IFS) is one of the effective tools for handling uncertainty. Among many extensions of IFSs, temporal intuitionistic fuzzy sets (TIFSs) are very interesting as they are time-dependent. Hence, TIFSs are suitable to define a video, which is dynamic and hence depends on time-moment. In this way, this work introduces a novel VIdeo PROCessing (VIPROC) algorithm, using temporal intuitionistic fuzzy sets to enhance videos, which is first of its kind in existence. The comparison is made with fuzzy contrast intensification operation. VIPROC algorithm is designed using contrast intensification operation for video enhancement. The results are encouraging in comparison with the original test videos. The results are discussed taking into account the several frames of the test video. Further, the proposed algorithm can be applied/extended to engineering applications like motion tracking, traffic detection systems, real time videos captured through mobile (hand-held) devices, and so on. As no such algorithms are existing which use TIFSs to process a video, the authors got motivated to design and develop VIPROC algorithm.

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.

A Survey on Settling Gridlock Utilizing Picture Handling Methods and Improvement of an Android Application

Our current situation makes this a severe issue. Traffic congestion on both major and minor routes is constant. A strong traffic control system is necessary in order to address these problems. Human and material resources are lost as a result of poor traffic management. Due to these recurring issues, interest is growing in finding new ways to address the congestion issue. Inadequate capacity, unrestrained demand, and protracted delays caused by traffic signals are only a few of the factors that contribute to traffic congestion. Magnetic loop detectors, infrared sensors, wireless sensor networks, and other devices are all used in modern technology. As opposed to These frequently occurring problems are generating interest in solving the congestion problem through various methods and methods. Traffic congestion occurs for a variety of reasons, including insufficient capacity, unrestricted demand, and long delays due to traffic signals. Modern technology uses wireless sensor network systems, magnetic loop detectors, infrared sensors and more. On the other hand, video and image processing methods offer many advantages over traditional systems. This article summarizes an overview of all available existing methods compared to those used in this field of research. We summarize all the various research papers based on different methods for the development of intelligent traffic detection systems using image and video processing methods. A comparison and overview of all methods is presented in this overview document.

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.

An edge implementation of a traffic sign detection system for Advanced driver Assistance Systems

An edge implementation of a traffic sign detection system for Advanced driver Assistance Systems

Traffic Sign Detection and Recognition Based on Convolutional Neural Network

As autonomous vehicles are developing and maturing the technology to implement the domestic autonomous vehicles. The critical technological problem for self-driving vehicles is traffic sign detection and recognition. A traffic sign recognition system is essential for an intelligent transportation system. The digital image processing techniques for object recognition and extraction of features from visual objects is a huge process and include many conversions and pre-processing steps. A deep learning-based convolutional neural network (CNN) model is one of the suitable approach for traffic sign detection and recognition. This model has overcome significant shortcomings of traditional visual object detection approaches. This paper proposed a traffic sign identification and detection system. The proposed design and strategy are implemented using the Tensorflow framework in google colab environment. The experiment is applied on the publicly available traffic sign data sets. The defined deep convolution neural network based model experimental results achieved 94.52% and 80.85% precision and recall respectively. Improving the seep of recognition and identifying appropriate features of traffic sign objects are addressed using deep learning-based encoders and transformers.