A Deep Learning Based Accident Detection System

Authors

  • S.Lalitha, Chandra Ananya, Gudesi Bhanusha, Yenuguwar Anjali

Keywords:

Deep Learning, Accident Detection, CNN, Image Processing, Object Detection, Neural Network Architectures, Video Analysis, Real-time Monitoring, Computer Vision, Edge Computing

Abstract

In this fast-paced world, the number of deaths due to accident is growing at an expeditious rate. Majorreasons for these accidents are rash driving, drowsiness, drunken driving, carelessness, etc. An indicator ofsurvival rates after detecting accidents is the time between the occurrence of accidents

References

https://towardsdatascience.com/a-comprehensive- guide-to-convolutional- neural- networks-the-eli5- way-3bd2b1164a53

https://arxiv.org/pdf/1905.05055.pdf#:~:text=Since% 20then%2C%20obje ct%20 ection%20started,%E2%80%9Ccomplete%20in%20 one%20step%E2%80%9D

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Published

2024-04-11

How to Cite

S.Lalitha, Chandra Ananya, Gudesi Bhanusha, Yenuguwar Anjali. (2024). A Deep Learning Based Accident Detection System . Journal of Computational Analysis and Applications (JoCAAA), 33(4), 751–755. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/2464

Issue

Vol. 33 No. 4 (2024): Selected Papers on Computational Techniques Across Multidisciplinary Fields: Innovations and Applications

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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