Advancements in Communication and Systems
Real-Time Accident Crash Detection using Machine Learning: A Survey
Authors: Subhash A. Nalawade, Shivam Kurhade, Sumakshi Khajuria, Preeti Landekar and Ishwar Barhate
Publishing Date: 20-01-2024
ISBN: 978-81-955020-7-3
Abstract
This survey article explores the development of crash detection systems and highlights the traditional dependence on antiquated technologies like GPS, GPRS, and Internet of Things hardware. These outdated systems have long struggled with inefficiencies, such as slow reaction times and a tendency to set off false alarms. The paper suggests a paradigm shift in response to these flaws by integrating contemporary deep learning-based object detection algorithms. These state-of-the-art algorithms process real-time image and video data by utilizing the power of neural networks and machine learning. This novel method has the ability to greatly enhance the accuracy of detecting the real-time accidents. The survey adopts a comprehensive strategy, addressing the difficulties encountered, suggesting the most recent developments in deep learning-based object detection, clarifying the drawbacks of earlier systems, and providing a direction beforehand for future research. This survey paper aims to close the existing gap between current crash detection systems and a future marked by increased efficiency and dependability on our roads by utilizing deep learning-based object detection algorithms. The paper concludes by highlighting the tremendous potential that adopting cutting-edge technology will improve the road safety.
Keywords
Neural network, Deep learning, Object detection
Cite as
Subhash A. Nalawade, Shivam Kurhade, Sumakshi Khajuria, Preeti Landekar and Ishwar Barhate, "Real-Time Accident Crash Detection using Machine Learning: A Survey", In: Ashish Kumar Tripathi and Vivek Shrivastava (eds), Advancements in Communication and Systems, SCRS, India, 2024, pp. 107-113. https://doi.org/10.56155/978-81-955020-7-3-9