ACCIDENT DETECTION USING ML AND AI TECHNIQUES

Rutik Desai1, Akash Jadhav2,Suraj Sawant3 ,Neha Thakur4
1BE Student, Information Technology, Datta Meghe College of Engineering, Navi Mumbai, Maharashtra
2 BE Student, Information Technology, Datta Meghe College of Engineering, Navi Mumbai, Maharashtra
3 BE Student, Information Technology, Datta Meghe College of Engineering, Navi Mumbai, Maharashtra
4 Professor, Information Technology, Datta Meghe College of Engineering, Navi Mumbai, Maharashtrair

Abstract – This paper employs computer vision in detecting accidents from CCTV footage and alerting Rescue systems. Computer vision is accelerating almost every domain within the industry. With the assistance of Computer Vision technologies, organisations are revolutionising the way machines accustomed to work earlier. Many times, after an accident occurs, people do not get Medical Aid services as early as possible or properly due to this lot of people losing their lives. In previous Technologies, Sensors are used for accident detection and alerts will be sent to the Relative of the Driver. But sensors can be damaged or sometimes it can not work. To overcome this we used Computer vision and AI techniques to detect Accidents and alert it through Android Application. We used the Yolov3 algorithm for detection of accidents and cars.

Key Words: YOLO, AI, ML, Firebase, OpenCV, Python.

1. INTRODUCTION

Computer vision is a field of study which enables computers to replicate the human visual system. It’s a subset of artificial intelligence which collects information from digital images or videos and processes them to define the attributes. The entire process involves image acquiring, screening, analysing, identifying and extracting information. This extensive processing helps computers to know any visual content and act on that accordingly.

The motor vehicle population is growing at a faster rate than the economic and growth of population. Accidents and also the death rate due to road accidents, especially two-wheelers are also increasing at an alarming rate. Most of the accident deaths that happen are due to the shortage of immediate medical assistance, on the roads like express highways. A facility for providing immediate medical assistance to the accident area can reduce the fatality to a greater extent. Thus comes the concept of an alert system that senses the accident and its seriousness to alert the nearby center for providing ambulance or medical aid to the accident area.

To note, previous literature mainly focused on sensors and traditional approaches and some of them used algorithms like Yolov3, RCNN and many detection algorithms.

In this Project, the CCTV system will check whether an accident has occurred and identify the accident. Once the selection of accidents has been taken, the system will check for the closest center, police station and notify them about the incident. The rescue team can rush to the spot immediately with no delay because the right location is getting to be communicated by the Android Application. The system will send geolocation of accidents through Android applications to rescue systems.
Accident detection and alert systems have been extensively studied over the past several years. Research adds this field has proposed a Telematics model which contains a main module. The CCTV system is supposed to capture the situation of the accident and through GPS, send the location information to rescue systems. Another main feature is, cops also get live CCTV footage of the accident place.

Considering these, a vision-based accident detection model framework was developed for accident detection and alert systems. The paper is organized as follows: the second section discusses previous literature related to vision-based accident detection and alert systems. Section 3 introduces the motivational and problem definition of the project. Section 4 discusses the designs and methodology of the project. Section 5 discusses the results and observations of the project. Section 6 concludes the findings of this research.

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