Integration of Image Segmentation and Fuzzy Theory to Improve the Accuracy of Damage Detection Areas in Traffic Accidents

Abstract Machine vision technology will have a significant impact on all industrial works in the next decade due to the latest technological advances in this field, and these advances are such that the use of this technology is now vital. Machine vision involves the process of applying a wide range of technologies and methods in providing automated inspection based on imaging, process control, and robot guidance in industrial applications. One of the applications of car 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.