Violation Detection System Research Articles

Hospital-based surveillance for Road Traffic Injuries (RTIs) in a tertiary care hospital

Road traffic accidents (RTAs) lead to significant mortality, morbidity and economic loss in India. The present study aims to characterize epidemiology of Road traffic injuries (RTIs) with information from multiple data sources and to describe factors associated with RTIs. Patients admitted with RTIs in a tertiary care hospital were included in this cross-sectional observational study. Hospital records, case sheets of RTA victims and interview of the participants with a questionnaire were used for collecting data. Majority (29%) of road traffic accidents occurred between 4pm and 8 pm, with 60% self-fall or skid. Around 23.8% consumed alcohol, 63.3% exceeded speed limit, 85% rescued within 30 minutes, 57% transported to hospital by government ambulance, 30% reached tertiary care directly, 6.5% had reduced GCS score. Young adult male were the major victims of RTAs with speed violation as a common risk behavior. The findings suggest a need for implementation of speed violation detection systems to bring down the occurrence of RTAs.

Real-Time Object Detection and Tracking for Occupational Health and Safety (OHS) in Mining Operations Using Streamed CCTV Data

Abstract Occupational Safety and Health (OHS) is very important in the mining industry which is prone to accident risks. Automatic detection of violations through CCTV streaming with the concept of a perimeter area is needed to prevent accidents. The purpose of this research is to create a violation detection system without re-modeling training that can adapt to different objects and regions. The proposed violation detection model includes four computational steps, namely (i) data reading, (ii) data pre-processing, (iii) object detection, and (iv) object tracking and violation detection. The computer vision technique uses the SIFT (Scale Invariant Feature Transform) algorithm and heuristics implemented with OpenCV. We used five CCTV video recordings of mining operations as examples. The results showed an average precision of 61.14%, a precision of 80.41%, and a recall of 69.38%. The average computation time of the model is 0.3 seconds per frame with heuristics that allow the model to be used in real-time. The model can be widely used in various fields and locations without having to retrain the model to improve OHS in the mining industry and other sectors with similar risks. This study will contribute to the development of a flexible, fast, and real-time automatic detection system.

Convolutional Neural Network and LSTM for Seat Belt Detection in Vehicles using YOLO3

The application of an electronic violation detection system has begun to be implemented in many countries using CCTV cameras installed at highway and toll road points. However, the development of a violation detection system using data in the form of images that have a high level of accuracy is still a challenge for researchers. Several types of violations detected include the use of seat belts, the use of cell phones while driving, which is influenced by the number of vehicles, vehicle speed and lighting, which can increase the difficulty in the detection process. This research developed a traffic violation detection system using a hybrid model, namely the CNN and LSTM algorithms for the application of discipline using seat belts. The dataset was obtained from RoboFlow Universe with a total of 199 front view car images consists of 82 using seatbelts and 78 not using seatbelts for the training process. The CNN algorithm plays a role in the feature extraction process from input image data, while the LSTM algorithm plays a role in the prediction process. Additionally, the performance evaluation of the CNN+LSTM algorithm will be measured using the accuracy value to measure the performance of the training process and testing process. When measuring the performance of the training process, it will be compared with several basic detection models used, such as CNN, VGG16, ResNet50, MobileNetV2, Yolo3, Yolo3+LSTM. The test results show that Yolo3+LSTM has a higher accuracy compared to the others, at 89%. Next, in the testing process, the CNN+LSTM model will be compared with the basic method, namely CNN. The test results show that the CNN+LSTM models have a higher accuracy of 89%. Meanwhile, in the basic CNN model, the resulting accuracy was 85%.

HELMET RULE VIOLATION DETECTION USING DEEP LEARNING

The widespread non-compliance of helmet-wearing regulations among motorcyclists in India poses a significant threat to road safety. This paper introduces a new approach to address this issue through the developing a Helmet Rule Violation Detection system. Given the challenges faced by traffic police in enforcing helmet regulations, the proposedsystem leverages computer vision techniques to automatically identify motorcyclists without helmets. The methodology involves the detection of moving vehicles through thresholding, followed by a classification process based on area and aspect ratio to distinguish between motorcyclists and non-motorcyclists. The subsequent identification of helmet violation is achieved by isolating relevant blobs and employing a classification mechanism. By automating this process, the system aims to reduce the burden on traffic police and enhance the overall safety of road users. The proposed approach not only contributes to the mitigation of helmet rule violations but also promotes responsible road behavior, fostering a safer and more compliant traffic environment in India.

Traffic Violation Detection System

Travel convenience is impacted by a number of factors, including the condition of the road, traffic, length of trip, accidents, speed, etc. The daily increase in accident rates poses the biggest concern in Sri Lanka. These events not only result in fatalities but also increase the nation's financial losses. Traffic congestion is brought on by road users' lack of discipline and sentiments, which may result in traffic offenses. The goals of this research are to curb any traffic infractions as well as reduce corruption and nepotism on the road. This system was developed as separate three models namely edge server, fine management system and centralized server. Edge server developed in python is to detect traffic violations such as parking, single line cutting, double line cutting, one way and u turn in real time according to the changing rules and constraints configured by the traffic police admin via the fine management system which supports a configurable platform designed using canvas designing tools. Upon edger server identifying violations, the functions in the centralized server responsible to recognize the number plate of the violated vehicle will be triggered and send a sms to driver for further fine management and payment supported by the fine management system. The mobile application controlling server is responsible to track and inform the violations to nearest police officer in any case the system could not rectify the vehicle number plate.

Traffic Signal Violation Detection System Using Computer Vision

Traffic Signal Violation Detection System Using Computer Vision

Traffic Violation Detection System on Two-Wheel Vehicles Using Convolutional Neural Network Method

The number of vehicles is increasing every year, and along with it, the number of traffic violations is also rising.. Traffic violations are one of the causes of traffic accidents. Currently, traffic violation detection still uses conventional methods, involving the police to take action. Preliminary research on traffic violation detection by several researchers mostly uses the Yolo Method. The study aims to design a traffic violation detection system for two-wheeled vehicles using the Convolutional Neural Network (CNN). In this research, the CNN method was used with the Faster RCNN architecture. Faster R-CNN is composed of convolution layers, Relu, and pooling layers which are used to extract features from images. An image in the size of 3264 x 1836 pixels, with the type of marking violation and helmet use was used as a sample. The number of images used was 660 images with 600 images for training and 60 images for testing. The system will detect traffic violations on two-wheeled vehicles, namely helmet use violations and road marking violations. This traffic violation detection system for two-wheeled vehicles produces the highest accuracy, namely 85% with a maxpooling kernel size value of 1x1, stride 1 and a learning rate of 0.003. This research has the potential to be applied to areas that are less accessible to the police, because the system will record and analyze violations.

Intelligent Vehicle Violation Detection System Under Human–Computer Interaction and Computer Vision

In view of the current problems of low detection accuracy, poor stability and slow detection speed of intelligent vehicle violation detection systems, this article will use human–computer interaction and computer vision technology to solve the existing problems. First, the picture data required for the experiment is collected through the Bit Vehicle model dataset, and computer vision technology is used for preprocessing. Then, use Kalman filtering to track and study the vehicle to help better predict the trajectory of the vehicle in the area that needs to be detected; finally, use human–computer interaction technology to build the interactive interface of the system and improve the operability of the system. The violation detection system based on computer vision technology has an accuracy of more than 96.86% for the detection of the eight types of violations extracted, and the average detection is 98%. Through computer vision technology, the system can accurately detect and identify vehicle violations in real time, effectively improving the efficiency and safety of traffic management. In addition, the system also pays special attention to the design of human–computer interaction, provides an intuitive and easy-to-use user interface, and enables traffic managers to easily monitor and manage traffic conditions. This innovative intelligent vehicle violation detection system is expected to help the development of traffic management technology in the future.

Traffic Rules Violation Detection System

Abstract: The paper presents a system that uses surveillance videos to automatically detect bike riders without helmets by us-ing object segmentation and background subtraction techniques. A consolidation approach is introduced to improve accuracy, and three feature representations are compared. The system achieves a detection accuracy of 93.80 percentage and has an average processing time of 11.58 milliseconds, making it a cost-effective and real-time solution for managing traffic violations related to helmet usage

APPLYING MACHINE LEARNING AND OBJECT DETECTION FORIDENTIFYING TRAFFIC RULE VIOLATION: OVER SPEEDING

We have observed that traffic offences are manually inspected by the traffic department in many developing nations, such as India. Such systems enable traffic management and traffic law enforcement. Following this, the present study focuses on Traffic violations as one of the key issues hindering the efficient operations and safety aspects in the urban traffic area. Currently, traffic violations are controlled using enforcement measure that demands a high manual workforce, and resource which entails high economic cost. Contrary, the use of automatic traffic violation detection systems in many of the urban cities of the developed and developing countries is being implemented for a higher degree of traffic enforcement and adherence to the traffic operation rules. Following this the present study focus on the applicationof machine learning for automatic traffic violation detection for weak- lane disciplined mixed traffic conditions. The resulting exhibit that the developed model can detect traffic violations of over-speeding at an accuracy of 95%. The traditional methods like speed camera use the basic principle of the Doppler Effect and RADAR technologies but at present,Due to their drawbacks which vary from the expense of purchasing equipment, increase in work and labour costs, and lack of monitoring, they are ineffective. As a result, it is a straightforward, affordable, and practical speed detecting method in Mixed traffic conditions. So here, we prefer automatic speed violation detection. The suggested solution will be highly accurate and cost-efficient.

Motorcycle Rules Violation Detection and Analysis System Using Deep Learning

Abstract: Nowadays motorcycle accidents are increasing day by day. Helmets are the most important and main safety equipment of motorcyclists .But in most of the accident cases, we can observe that most of the motorcyclists might not have worn a helmet. The main aim of wearing helmets is to protect the driver’s head when an accident occurs. The existing system lacks an efficient mechanism for detecting and monitoring violations of motorcycle rules such as riding triples, not wearing helmets, and over-speeding. There is a need for an automated system that can accurately identify and track motorcycles violating the rules, enabling law enforcement agencies to take appropriate action. The current process of manual identification and reporting of violations is time-consuming, prone to errors, and does not provide real-time insights on the frequency and distribution of violations across different areas. Law enforcement authorities require a comprehensive analysis of the violations per area to understand the problem areas and allocate resources effectively for enforcement and awareness campaigns.

APPLICATION OF VARIOUS DEEP LEARNING MODELS FOR AUTOMATIC TRAFFIC VIOLATION DETECTION USING EDGE COMPUTING

A rapid growth in the population and economic growth has resulted in an increasing number of vehicles on road every year. Traffic congestion is a big problem in every metropolitan city. To reach their destination faster and to avoid traffic, some people are violating traffic rules and regulations. Violation of traffic rules puts everyone in danger. Maintaining traffic rules manually has become difficult over the time due to the rapid increase in the population. This alarming situation has be taken care of at the earliest. To overcome this, we need a real-time violation detection system to help maintain the traffic rules. The approach is to detect traffic violations in real-time using edge computing, which reduces the time to detect. Different machine learning models and algorithms were applied to detect traffic violations like traveling without a helmet, line crossing, parking violation detection, violating the one-way rule etc. The model implemented gave an accuracy of around 85%, due to memory constraints of the edge device in this case NVIDIA Jetson Nano, as the fps is quite low.

Traffic Signal Violation Detection System

Traffic Signal Violation Detection System

Using a police warning system to eliminate errors in intelligent violation detection systems

A broad range of diverse technologies under the generic topic of intelligent transportation systems holds the answer to many transportation problems. Tailgating and the failure to maintain sufficient distance from the next vehicle are among the biggest causes of motorway accidents. There are intelligent systems to detect this violation. Their insufficient accuracy is, however, the reason they are used with reluctance. In this research, a police warning system is proposed to note the detected violating drivers by a short message service. If the violation is repeated, the driver will be fined. Using probability logic, it has been shown that by the proposed system, we can be sure that the error in traffic ticketing will be very low. A survey of police officers found that most of them welcome the proposed system.

Social distance monitoring of site workers for COVID-19 using context-guided data augmentation, deep learning, and homography transformation

Because of the COVID-19 pandemic, many industries have developed efforts to minimize COVID-19’s spread. For example, the construction industry in Melbourne practices social distancing and downsizes the number of workers on the job site. The surveillance system integrated with deep learning models has been extensively utilized to enhance construction safety. However, such 2D-based approaches suffer from occlusions, and the workers may not be accurately detected under this circumstance. To this end, this paper proposes a novel context-guided data augmentation method to enhance deep learning models’ performance under occlusions. The context-guided method can automatically augment images by adding occlusions to the objects. Using this way, deep learning models can learn the object’s features in various occlusion scenarios. Later, this method is validated by a real-time social distancing violation detection system. Specifically, this system utilizes a modified YOLOv4 model to detect workers by bounding boxes. Then, the DeepSORT algorithm is used to track the worker trajectories. Finally, homography transformation is used to calculate the distance between workers in each frame. The system has revealed robust results using the data augmentation method, and promising results indicate that the system can well support worker health during COVID-19.

Automatic detection system of speed violations in a traffic based on deep learning technique

With the growth in vehicular traffic, there is a greater risk of road accidents. Over speeding, intoxicated driving, driver distractions, red-light runners, ignoring safety equipment such as seat belts and helmets, non-adherence to lane driving, and improper overtaking are the leading causes of accidents. Speed violation, in particular, has a significant influence on today’s transportation. Also, detecting this speed violation and punishing this violator are more time-consuming tasks. For that reason, a novel automatic speed violation detection in traffic based on Deep learning is proposed in this paper. This proposed method is separated into two working modules: object detection and license plate recognition. The object detection module uses the most efficient PP YOLO neural networks. It utilizes open ALPR (Automatic License Plate Recognition) for the vehicle’s number plate identification, which passes the traffic above maximum speed. With the number plate details, the authorities can take action against the rule violator with less time and effort. The simulation results show that the proposed automatic speed violation detection system also has an accuracy rate of 98.8% for speed violation detection and 99.3% for license plate number identification, demonstrating that the approach described in this work has a higher performance in terms of accuracy. Furthermore, the proposed technique was compared to recent existing results.

Smart Traffic Signal Violation Detection System Based on IOT

Roads are primary route to transportation in each country, and they are frequently characterized by traffic congestion due to a lack of traffic management. It could be due to the big population, lack of technology, and violations committed by humans, the most serious of which is traffic rule infringement. It is extremely difficult for authorities to determine whether or not there is a traffic rule infringement, resulting in horrible scenarios that endanger not just drivers but pedestrians as well. To apprehend car owners who disregard traffic signal limits, the traffic police need a straightforward strategy. The administrators are currently checking the film that is being recorded near the traffic signal 24 hours a day, 7 days per week to see if anyone has disregarded the traffic light laws. After taking a picture of the license plate, they must report. Technology can play a big part in addressing these violations, which is why our work focuses on IoT and image processing-based methods. The suggested system's camera records a picture of the car, analyzes the video using the right image processing algorithm, and then records the license plate of the vehicle that violated the traffic laws and transmits it to the administrators. As a result, less manual work is required from police managers, and the video analysis takes place directly on the edge device.

IoT Based Signal Violated Vehicle Detector

Abstract: The number of new vehicles on the road is increasing rapidly, which in turn causes highly congested roads and due to the long awaiting red signals, few vehicle drivers ignore traffic rules by violating traffic signals. This leads to a high number of road accidents and hence it is essential to detect the signal violated vehicles automatically. Traffic violation detection systems using IOT technology can able to send violated vehicle information to the concern traffic police station automatically by which the vehicle can be penalized immediately. The proposed system can be implemented using vehicle ID card installed in the vehicle itself such that if the vehicle moves further during presence of red signal, automatically, vehicle information in the form of vehicle registration number, type of vehicle (weather it is two-wheeler, car, truck, auto, etc.), owner name, vehicle colour, etc, can be forwarded to the concern authorities through concern mobile phones. To prove the concept practically, all vehicles must be equipped with these low-cost wireless ID cards such that the vehicle data can be transmitted continuously. The demo module contains a mini automatic traffic signal post along with simulated road. The system is designed such that when red signal is energized and during this period, if any vehicle crosses the zebra lines, the system receives the vehicle data and transmits the same through Wi-Fi module. If anybody violates the traffic signal, immediately alarm will be energized to alert the nearest traffic police. The main processor must be installed near the traffic signals, which regrets to acquire vehicle information during the presence of yellow and green signals. The demo module contains one toy car which will be equipped with its wireless ID card. Major building blocks: Simulation of single lane road constructed with traffic signal post & zebra crossing, main processor designed with Arduino MCU, wireless ID card designed with 89C2051 controller chip, IR signal decoder (TSOP1738), Wi – Fi module, Alarm, IR LED, 5v Power supply unit, Toy car, etc.

Automatic Traffic Red-Light Violation Detection Using AI

Our research is the design of a traffic signal violation detection system using machine learning that learns to prevent the increasing number of road accidents. The system is optimized in terms of accuracy by using the region of interest and location of the vehicle with a red-signal state. By modifying some parameters in the YOLOV5s and re-training the COCO dataset, we can create a model which can be predicted with a high accuracy of 82% for vehicle identification, 90% for traffic signal status change and up to 86% for violation detection. This can be used for red light violation detection which will help the traffic police on traffic management.

Deep learning‐based substation remote construction management and AI automatic violation detection system

Workplace video surveillance and timely response to operational violations are critical to avoid operator injuries at power construction sites. Here, a system that combines remote substation construction management and artificial intelligence object detection techniques to intellectualize the power construction management process and identify violations during construction in real time is proposed. To improve the detection accuracy, a data augmentation method, including three operations: (1) object segmentation and background fusion; (2) partial erasing; and (3) other basic transformations, is also proposed. Six variants of the You Only Look Once (YOLO) model are trained as detectors for comparative experiments on a dataset collected at the practical construction site. The experimental results show that the detection precision and recall of the YOLOv5-s model are 0.852 and 0.922, with high accuracy and low miss rate, which meet the requirements of robustness and accuracy in detecting realistic power construction violations.