Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents

Journal of Big Data

Mahboub Parhizkar¹  Majid Amirfakhrian² 

[1] Department of Mathematics, Central Tehran Branch, Islamic Azad University, Tehran, Iran;University of Calgary, Alberta, Canada;

关键词: Machine vision;    Image processing;    Image segmentation;    Dimensional reduction;    Crash damage detection;   

DOI  :  10.1186/s40537-021-00539-2

来源: Springer

【 摘 要 】

In the next decade, machine vision technology will have an enormous impact on industrial works because of the latest technological advances in this field. These advances are so significant that the use of this technology is now essential. Machine vision is the process of using a wide range of technologies and methods in providing automated inspections in an industrial setting based on imaging, process control, and robot guidance. One of the applications of machine vision is to diagnose traffic accidents. Moreover, car vision is utilized for detecting the amount of damage to vehicles during traffic accidents. In this article, using image processing and machine learning techniques, a new method is presented to improve the accuracy of detecting damaged areas in traffic accidents. Evaluating the proposed method and comparing it with previous works showed that the proposed method is more accurate in identifying damaged areas and it has a shorter execution time.

【 授权许可】

CC BY