@article{SONG2023, 
author = {Le SONG and Yupeng HOU and Junpeng ZHANG and Tong WU and Haoming QI and Enhao SHANG},
title = {Monocular visual 3D cuboid measurement method based on Mask-RCNN and SFM},
year = {2023},
journal = {Journal of Measurement Science and Instrumentation},
volume = {14},
number = {2},
pages = {127-136},
keywords = {deep learning, Mask-region convolutional neural networks (Mask-RCNN), monocular vision, structure from motion (SFM), 3D measurement},
url = {https://www.sciopen.com/article/10.62756/jmsi.1674-8042.2023015},
doi = {10.62756/jmsi.1674-8042.2023015},
abstract = {To solve the limitation of multi-view in motion-based reconstruction method and realize automatic measurement in three dimensions, a monocular vision method based on Mask-region convolutional neural networks (Mask-RCNN) and structure from motion (SFM) is proposed for the 3D cuboid measurement. Taking a box as example, the method mainly includes measurement point extraction, transformation matrix calculation and 3D mapping measurement. Only one calibration is required, and 3D measurement using deep learning techniques and single-view images can be automatically realized. The method avoids complex reconstruction and reduces the application requirement of visual measurement methods. Experimental results show that the uncertainties of relative standard obtained by this method under checkerboard and specific markers are less than 6% and less than 8%, respectively.}
}