Date of Award

Spring 2022

Document Type


Degree Name

Bachelor of Science (BS)


Physics and Engineering Science


College of Science

First Advisor

Xiangxiong Kong


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.