Date of Award
Spring 2022
Document Type
Thesis
Degree Name
Bachelor of Science (BS)
Department
Physics and Engineering Science
College
College of Science
First Advisor
Xiangxiong Kong
Abstract/Description
Routine bolt-loosening inspection plays an essential role in managing and preventing the degradation of our nation’s highway bridges over time. Neglecting to perform these inspections could result in public safety concerns. The study of this thesis develops a cost-effective method of bolt-loosening detection based on computer vision. To this end, two input images of the bolted connections are collected at two different inspection times. The feature points are then identified from the input images, based on which a geometric transformation matrix is applied to correct any perspective differences between the two images. Next, we select the image patches of the loosened bolt and apply the geometric transformation again to quantify the angle of the bolt head’s rotation. We validated our method through a laboratory test and test results show the success of our method in quantifying the loosened bolt.
Recommended Citation
Burdette, Savannah, "A Computer Vision-Based Method for Bolt Loosening Detection" (2022). Honors Theses. 436.
https://digitalcommons.coastal.edu/honors-theses/436