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Title |
Study on Noise Reduction in Non-Attached, Non-Contact Displacement Detection Technology Using AI
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Authors |
김종국(Jong-Guk Kim) ; 최우석(Woo-Suk Choi) ; 백승훈(Seung-Hoon Baek) ; 우욱용(Ukyong Woo) ; 최하진(Hajin Choi) |
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DOI |
https://doi.org/10.11112/jksmi.2025.29.2.25 |
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Keywords |
인공지능; 비부착; 비접촉; YOLO; 계측방법 AI; Non-attachment; Non-contact; YOLO; Measurement method |
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Abstract |
For displacement measurement of cultural properties and important structures, new technologies based on non-contact, non-attachment methods are required instead of traditional contact-based measurements. This study developed an image-based measurement algorithm with a resolution of less than 0.1 mm at a 1-meter distance. A laser-generated virtual grid system was designed to monitor structures through imaging, and the system was tested and operated over four years of data collection at various facilities. The measurement device was verified for resolution and accuracy through indoor testing, but field deployment faced challenges such as noise due to varying light conditions between day and night, as well as adverse weather conditions like rain and fog. To enhance I mage reading accuracy, an AI algorithm was applied. Specifically, the YOLO algorithm was utilized for automatic recognition of the virtual grid pattern, using a dataset consisting of a total of 6,512 images from field conditions, including normal and noisy samples, achieving an Intersection over Union (IoU) score of 0.9. The application of the developed algorithm demonstrated improved daily data variance, reducing from ±40 pixels (1.6mm) to ±20 pixels (0.8mm), thereby validating the noise reduction effectiveness through AI integration.
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