An Intelligent Vision-Based Bubble Level Measurement System Using AI
Year: 2026
Type: conference-paper
Abstract: Conventional bubble levels suffer from human parallax and lack digital interfaces, while high-precision electronic levels are expensive. This study proposes an intelligent AI vision-based bubble-level measurement system that uses AprilTag for camera pose correction and a lightweight deep learning model for real-time image recognition. Experiments verify that the system provides resolution beyond the human visual limit and supports real-time data transmission for low-cost, high-precision intelligent equipment calibration.
Authors: Yu-Sen Cheng, Chia-Yu Chen, Kai-Ming Uang, Tung-Hsien Hsieh, Yung-Hoh Sheu
Institution: National Formosa University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7204149359