A Real-Time Collision Detection System for Vehicles

被引：1

作者：

Amiri, Sam ^{[1
]}

Singh, Shailendra ^{[1
]}

机构：

[1] Coventry Univ, Fac Engn Environm & Comp, Coventry, W Midlands, England

来源：

INTERNATIONAL CONFERENCE ON ELECTRICAL, COMPUTER AND ENERGY TECHNOLOGIES (ICECET 2021) | 2021年

关键词：

Collision Detection System; Object Detection; Pedestrian Detection; Cyclist Detection; Vehicle Detection; Machine Learning; Deep Learning; Convolutional Neural Network; Mask-RCNN;

D O I：

10.1109/ICECET52533.2021.9698622

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：1194 / 1199

页数：6

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