International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023
p-ISSN: 2395-0072
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Accident Precaution System For Vehicle In Motion Using Machine Learning Saniya Shaikh1, Omkar Prabhu2, Rahul Patil3, Somesh Nikam4, Prof.Sachin Chavan5 1,2,3,4Student at Mahatma Gandhi Mission College of Engineering and Technology, Mumbai, Maharashtra, India.
5Assistant professor at Mahatma Gandhi Mission College of Engineering and Technology, Mumbai, Maharashtra, India.
---------------------------------------------------------------------***--------------------------------------------------------------------Abstract - Nowadays speed and breaching traffic rules causes many accident. we can save many live if we provide accident information to emergency service and if we reach in time. heavy road traffic and increasing number of road accidents are major concern in current scenario rather than new vehicle have latest technology. Our survey on this topic is made to construct such a system which is efficient and reliable to detect danger while our vehicle in motion. In this paper, we try to overcome the problem by create system "Accident Precaution System For Vehicle In Motion Using Machine Learning" using Deep learning and machine learning algorithm such as Convolutional Neural Network (CNN), Artificial Neural Network (ANN), YOLO (you look only once).Using these algorithm we develop different model such as Driver Drowsiness, object detection, pothole detection and traffic sign detection for decrease the possibility of accident.
because there vehicle in motion so deep learning algorithm such as Convolutional Neural Network(CNN) which focus on particular region can help in fast image processing[1]. Some causes for accident is Driver fall in slip while driving, it happen mostly in night driving.it may cause serious crash so if we analyze driver face and detect facial feature in real time it will overcome the danger of an accident[3]. Another obstacles on Road also cause accident such as vehicle can crash with each other so vehicle can detect using technology such as R-CNN, CNN, Darknet for detect and analyze vehicle from surrounding in safe distance [9]. In this paper, we proposed systems that previously detect Road objects, Traffic sign and Driver Drowsiness using various Deep learning and machine learning technology with tensorflow and image processing. It will help in Autonomous vehicle creation and make driving safe and secure for Driver and passenger.
Key Words: Deep learning, Machine learning, Convolutional Neural Network (CNN), Artificial Neural Network (ANN), YOLO(you look only once),python, Computer Vision.
2. RELATED WORK In Existing system, models are built for accident prevention such as Drowsiness Detection, road object detection and traffic sign detection for create pre-alarm system that help driver in his journey. These models are built separately and used dataset that is limited. As we built different models we see different work separately as follow:
1. INTRODUCTION Automobile has provide a great benefits in our daily life. We use vehicle to reach destination on time. In 21st century it hard to imagine life without vehicle. There are various types of vehicle such as car, bus, truck etc. each used for different purpose, But every coin had two side that way increasing number of vehicle on road provide us benefits of fast transportation and decrease our travel time but it also cause disaster to us and may kill us through serious accident. Inappropriate driving and over speeding causes risk to involve in accident. Many efforts taken by various organization and government to decreases number of accident but still so many accident happen daily. We can save many life by provide emergency information about upcoming danger while driving. According Data of Ministry of Road Transport & Highways of India major reason for accident is Over Speeding, Distractions of Driver, Traffic Light Jumping, Non-adherence to lane driving and overtaking in a wrong manner.
Traffic sign detection In paper[1] they built model on DFG dataset which had different types of traffic signs, some of which give warning, mandatory and prohibitive instructions on how and where to drive. To trained and built the model they used region-based neural network which focus on particular region for detect traffic sign. In paper to built traffic sign detection we had to done pre-image processing as mention in paper [2].In this they used Image enhancement technology for clear shapes using linear filtering algorithm. Driver Drowsiness In previous study [3] To monitor and warn the driver in real-time, the use of the kernelized correlation filters (KCF) algorithm is preferred based on system’s evaluation. For real
As most of accident occurs because traffic rules properly not follow.so many drivers they actual not seen traffic light
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