@inbook{14823810a30d42ba817adf31eee92017,
title = "A Real-Time Collision Detection System for Vehicles",
abstract = "A real-time collision detection system has become a crucial safety feature in vehicles today, mainly after the evolution of autonomous and self-driving vehicles. It is proved to be very effective in minimizing the number of road accidents. This paper presents an algorithm for a real-time detection system using the deep learning technology based on Mask-RCNN (Mask-Region based Convolutional Neural Network). We prepared a custom dataset from scratch to experiment with our algorithm and a detailed analysis of the results are provided. Experiments indicate that the developed algorithm gives highly accurate results. We achieved more than 95% accuracy with overall prediction score of greater than 0.90.",
keywords = "Collision Detection System, Object Detection, Pedestrian Detection, Cyclist Detection, Vehicle Detection, Machine Learning, Deep Learning, Convolutional Neural Network, Mask-RCNN",
author = "Sam Amiri and Shailendra Singh",
year = "2022",
month = feb,
day = "11",
doi = "10.1109/icecet52533.2021.9698622",
language = "English",
isbn = "978-1-6654-4232-9",
series = "2021 International Conference on Electrical, Computer and Energy Technologies (ICECET)",
publisher = "IEEE",
booktitle = "International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021",
address = "United States",
note = "2021 International Conference on Electrical, Computer and Energy Technologies, ICECET ; Conference date: 09-12-2021 Through 10-12-2021",
}