Accident Detection Research Articles

A Comprehensive Review on Accident Detection Using Artificial Intelligence and Machine Learning

This paper reviews the role of Artificial Intelligence (AI) and Machine Learning (ML) in accident detection systems, focusing on the application of image processing techniques and Convolutional Neural Networks (CNNs) to enable detection. Through a comprehensive analysis of recent methodologies, challenges, limitations, and future research directions, this paper aims to provide a detailed understanding of how AI and ML are advancing the field of accident detection

A novel weighted majority voting-based ensemble approach for detection of road accidents using social media data

A novel weighted majority voting-based ensemble approach for detection of road accidents using social media data

A Deep Learning-Based Car Accident Detection Framework Using Edge and Cloud Computing

The evolving technological landscape has seen a pivotal shift with the advent of edge computing, transforming various sectors, particularly accident detection. Edge computing enhances road safety by enabling realtime data processing from onboard sensors, cameras, and connected devices, addressing limitations in traditional cloudbased systems. This paper introduces a deep learning-based accident detection framework within an edge-cloud setup. Utilizing a CNN model, accidents are detected at the edge node near the data source, ensuring low latency, reduced network usage, and faster execution times. The model achieves a remarkable 95.91% accuracy.

Advancing Road Safety Through Intelligent Vehicle Accident Detection and Reporting Enabled by IoT Innovation

The escalating incidence of road accidents poses a significant challenge to public safety, often exacerbated by delays in emergency response. This research introduces an IoT-based intelligent system designed to revolutionize accident management by providing real-time data and critical information to emergency services. By seamlessly integrating Raspberry Pi and Arduino microcontrollers with advanced sensors such as force-sensitive resistors (FSRs), GPS, and potentially additional sensors like accelerometers and gyroscopes, the system accurately detects accidents, assesses their severity, and captures comprehensive data, including passenger images, vehicle details, and precise accident location. Upon impact, the system rapidly transmits this data, along with pre and post-accident images, to a designated emergency response center via a secure and reliable communication network. This swift dissemination of critical information empowers emergency responders to make informed decisions, optimize resource allocation, and prioritize response efforts, ultimately saving lives and minimizing the impact of accidents. The system's intelligence lies in its ability to not only detect accidents but also to assess their severity based on sensor data analysis, enabling a more targeted and efficient emergency response. Furthermore, the system's potential to collect valuable data on accident patterns and causes can contribute to the development of preventive measures and road safety strategies. By leveraging the power of IoT technology, this research aims to significantly enhance road safety and transform accident management practices.

Real-Time Freeway Crash Detection Framework using Connected Vehicle Waypoint Data

Accurate and timely detection of traffic accidents is crucial for transportation agencies seeking swift responses and effective traffic management, particularly on freeways where crash severity and traffic flow disruptions are amplified. While several methods have been developed using cameras or infrastructure-mounted sensors, these approaches may face challenges in geographical scalability. To address this issue, a novel approach has been developed that leverages real-time connected vehicle trajectories at a market penetration rate of 4%–9%. The method involves extracting five key features from individual journeys that sense the speed and acceleration fluctuations. The results demonstrate promising outcomes, achieving an overall accuracy of 63.5% with 4.1% false detections and 33.4% non-detections. This accuracy surpasses the speed-alone model using the same data by 24%. Furthermore, higher traffic volumes lead to even greater accuracy, exceeding 80% at level of service D and 90% at level of service E. The developed algorithm also exhibits precision in crash detection, with a mean latency of only 2.5 min after the actual incidents. The entire processing time may additionally add up to 1 min. The robustness of the model is tested for a single day on one of the interstates, providing promising results. This study highlights the potential of connected vehicle data in enhancing crash prediction methods and underlines its value for the safety and efficiency of future transportation systems.

Efficient Deep Learning Methods for Detecting Road Accidents by Analyzing Traffic Accident Images

Speed is one of the major factors in car crashes. Many lives could have been saved if emergency services had been alerted to the disaster and arrived in time. For the sake of protecting valuable human lives, an effective automatic accident detection system with prompt reporting of the accident scene to emergency services is essential. Therefore, this research proposes some effective Deep Learning techniques that properly recognize the incidence of accidents. The paper introduces two different techniques for image classification, with a particular focus on distinguishing between accident and non-accident images. The dataset used for the proposed model is taken from Kaggle, which is a collection of CCTV images. The first approach is a hybridized TL-ML method that employs transfer learning techniques that use different pre-trained versions of convolutional neural networks to extract features from image datasets. These extracted features are then fed into various machine learning classifiers to categorize the images as either Accident or Non-accident. To make the final decision, a voting classifier is utilized to choose the best classification outcome from the set of previously employed machine learning classifiers. In the second method, a modified Convolutional Neural Network (CNN) called SpinalNet is adopted. The performance of these models was evaluated by comparing them with each other and with a customized CNN base model. SpinalNet consistently surpassed the other models in terms of Precision, Recall, F1-Score, and Accuracy, demonstrating its outstanding capabilities.

Comparative analysis of machine learning techniques for enhanced vehicle tracking and analysis

The past few years have seen a marvellous growth in technology and science. This rapid improvement has proven to be a blessing, making human life easier. Technological developments such as autonomous driving systems and electric cars have made it easier to travel in a dependable and economical manner, satisfying the increasing need for convenient and environmentally friendly travel. However, the increase in traffic has led to a surge in accidents and road casualties. Despite efforts to enhance automobile design and traffic control, there remains a significant need for implementing a system for vehicle tracking, accident detection, and notification. Delays in information and unfulfilled medical needs often result in the loss of lives following accidents. This study reviews and compares different automatic accident detection and notification systems that use accelerometers, vibration detectors, and GPS technology to notify registered contacts of an accident's location via SMS or email. The analysis that follows will specifically look at the benefits, drawbacks, and future uses of various technologies that are used in these systems. In this study, different machine learning-based methods for improving the accuracy of car tracking and cutting down on reaction times in accident situations will be looked at and compared. For testing their usefulness, we used deep learning models like CNN, SVM, and YOLOv3 on a number of different datasets. According to our data, these methods greatly enhance the accuracy of spotting, with YOLOv3 showing the best level of accuracy. Furthermore, the study talks about the pros, cons, and possible future uses of these technologies. It stresses the need for more research into improving model performance in different situations.

Iris Detection Using Deep Learning and OpenCV

The proposed system's efficacy is evaluated through extensive simulations and real- world testing, demonstrating high accuracy in accident detection and timely activation of the emergency response. This integration of deep learning with OpenCV and an automated alert system aims to reduce response times and enhance the chances of survival and support in the critical moments following a car accident. Keywords--- Deep Learning, OpenCV

Deep Learning Model for Detection and Severity Analysis of Car Accidents

Abstract On-road car accidents are immensely unfortunate but quite common occurrences worldwide. Instant data-centric and informed decisions of crisis management are rarely experienced due to the absence of real-time car accident detection and severity analysis mechanisms. On this background, the current paper presents a deep learning model for car accident detection and analysis of its severity so that the crisis management activities might follow without any delay saving invaluable human lives. The existing works lack in using time-series data, the proper learning model for accurate prediction, and minimizing the time taken in post-accident scenarios for the victims to receive immediate medical help. This paper introduces the Long Short Term Memory (LSTM) model in conjunction with the Gradient Boosted Regression Trees (GBRT) technique for the determination of car accidents with different levels of severity. The proposed model works with the accelerometer and gyroscopic data collected through an application installed in the smartphones of the users inside the car. The LSTM-GBRT hybrid model is proposed to achieve higher accuracy than LSTM which deals with time-variant data. The satisfactory performance of the proposed technique has been reported and the results are extensively investigated in comparison with another hybrid technique such as LSTM with Random Forest (RF) as well. The statistics confirm the superiority of the proposed model over other parallel models in terms of several performance metrics, like Accuracy, Precision, etc.

Microcontroller-based camera with the sound source localization for automated accident detection

Microcontroller-based camera with the sound source localization for automated accident detection

TADS: a novel dataset for road traffic accident detection from a surveillance perspective

TADS: a novel dataset for road traffic accident detection from a surveillance perspective

Vision based Accident Detection System Using AI ML and Yolov8 Algorithms

Vision based Accident Detection System Using AI ML and Yolov8 Algorithms

Review of Accident Detection Methods Using Dashcam Videos for Autonomous Driving Vehicles

Review of Accident Detection Methods Using Dashcam Videos for Autonomous Driving Vehicles

Vehicle Accident Detection and Notification System

As it is observed these days, the roads are crowded with different kinds of vehicles, and as a result, accidents have become more dangerous every day. In this paper, a vehicle accident detection and notification system is designed by using road surveillance camera data and machine learning techniques to use the recorded footage from the surveillance cameras to detect the accident status, activate an alerting sound, and send the notification message as a Google email (Gmail) to the specialist. The system model was made by combining two algorithms, convolutional neural networks (CNN) and support vector machines (SVM), to make a CNN-SVM hybrid model, which was then trained using two datasets. The first dataset contains 4814 images with sizes (28, 28) and extensions (jpg), and the second dataset contains 990 images with sizes (32, 32) and extensions (jpg). The outcomes scores of the evaluation matrices are: accuracy for the first dataset is 99.74% and loss is 1.14%, and for the second dataset, accuracy is 98.88% and loss is 3.39%. When testing it with real-world data, it achieved its objectives in 30 seconds, with accuracy reaching 100%.

Auto Rescue: Instant Accident Detection and Emergency Response

This paper investigates how combining IoT and Machine Learning can improve vehicle monitoring and safety. It focuses on real-time accident detection and rapid emergency response to reduce road fatalities and injuries.

Experimental Verification on Deep Learning based Monitoring Algorithms for Early Detection of Damage in Buried Pipelines

Recent increases in buried pipeline damage accidents due to third-party interference have significantly heightened attention towards buried pipeline monitoring. Especially, as the sudden damage can lead to large-scale leakage, there is a necessity for preemptive response and maintenance. However, the application of a structural health monitoring approach is difficult, since the extensive network of buried pipelines, stretching over thousands of kilometers, exhibits diverse noise environments and propagation characteristics. As a result, challenges within the buried pipeline system frequently lead to damages being overlooked. In this study, introduces a deep learning-based pipeline damage monitoring algorithm, specifically designed to early detection of accidents caused by third-party interference. This algorithm integrates a CNN-based anomaly detection model, advanced signal processing for data preprocessing, and TDoA-based source localization. The training and test data set are the acquisition under completely independent conditions, which has been experimentally validate for applicability across various environments for buried pipelines. Moreover, both the training and test dataset acquisition were performed using accelerometers on in-service buried pipelines, each with diameters of 1,100 mm, 1,200 mm, and 2,200 mm, extending over lengths ranging from approximately 200 to 500 meters. Despite the independent conditions of the datasets, our study yielded over 95% accuracy in early detection, with the results being in good agreement with the actual excavate locations.

CAR ACCIDENT DETECTION UISING DEEP LEARNING WITH ALERT SYSTEM

The proposed system's efficacy is evaluated through extensive simulations and real-world testing, demonstrating high accuracy in accident detection and timely activation of the emergency response. This integration of deep learning with OpenCV and an automated alert system aims to reduce response times and enhance the chances of survival and support in the critical moments following a car accident. Keywords--- Deep Learning, OpenCV, Emergency Call, Message Notification, Email Alert

Safety Helmet with Collision Detection System

A comprehensive smart helmet system integrating various sensors and communication modules has been developed for enhancing safety and functionality during motorcycle rides. The research addresses the critical issue of road safety for motorcyclists by introducing an accident detection system using accelerometers and gyroscopes, coupled with an emergency response system. The implementation utilizes an ESP32 microcontroller, MPU6050 sensor, GSM module, and GPS module. The findings showcase the effectiveness of the collision detection system and its potential to significantly improve emergency response times. The abstract underscores the importance of such technological interventions in reducing accident-related fatalities and injuries among motorcyclists. The discussion emphasizes the need for wider adoption of similar smart helmet systems and highlights the potential for future enhancements in both hardware and software components. The outcomes suggest a paradigm shift in motorcycle safety measures, urging policymakers and manufacturers to consider integrating these technologies into standard safety protocols.

A Novel Integral Image Recognition Method and System with Verification Measurement by Sensors for Hot Steel-Bar Stack Accident Detection

A Novel Integral Image Recognition Method and System with Verification Measurement by Sensors for Hot Steel-Bar Stack Accident Detection

Smart Accident Detection and Emergency Response System for Enhanced Road Safety

In the realm of road safety, prompt and accurate accident response is paramount to mitigating fatal injuries and saving lives. The existing approach, reliant on manual reporting to authorities or the injured party's ability to communicate with emergency services, introduces delays that can prove fatal. The presented system uses a combination of IoT modules to detect the accident and alert the EMS. It uses an accelerometer to detect the accident, a sim module equipped with a 2G sim to send an alert message consisting of the drivers details, car details, and crash location, acquired by the GPS module to the authorities and few selected individuals. The goal is to drastically reduce emergency response time, improving the likelihood of survival and quick recovery of the injured. This innovative approach not only addresses the current gaps in accident reporting but also contributes to an overall improvement in road safety infrastructure. This work uses a collection of sensors to identify an accident and point out the accident location. This data is sent as a text message to emergency services through which immediate help can be received.