Next Article in Journal
Strongly Focused Circularly Polarized Optical Vortices Regulated by a Fractal Conical Lens
Previous Article in Journal
Influence of Different Freezing Modes on the Base Slab Displacement of an Upper Structure
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 10
Issue 1
10.3390/app10010026
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Dimension Measurement and Key Point Detection of Boxes through Laser-Triangulation and Deep Learning-Based Techniques

by
Tao Peng
1,
Zhijiang Zhang
1,
Fansheng Chen
2 and
Dan Zeng
1,*
1
Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, 99 Shangda Road, Shanghai 200444, China
2
Key Laboratory of Intelligent Infrared Perception, Chinese Academy of Sciences, Shanghai 200444, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(1), 26; https://doi.org/10.3390/app10010026
Submission received: 18 November 2019 / Revised: 11 December 2019 / Accepted: 13 December 2019 / Published: 18 December 2019
(This article belongs to the Section Optics and Lasers)
Browse Figures
Versions Notes

Abstract

Dimension measurement is of utmost importance in the logistics industry. This work studies a hand-held structured light vision system for boxes. This system measures dimension information through laser triangulation and deep learning using only two laser-box images from a camera and a cross-line laser projector. The structured edge maps of the boxes are detected by a novel end-to-end deep learning model based on a trimmed-holistically nested edge detection network. The precise geometry of the box is calculated by the 3D coordinates of the key points in the laser-box image through laser triangulation. An optimization method for effectively calibrating the system through the maximum likelihood estimation is then proposed. Results show that the proposed key point detection algorithm and the designed laser-vision-based visual system can locate and perform dimension measurement of measured boxes with high accuracy and reliability. The experimental outcomes show that the system is suitable for portable automatic box dimension online measurement.
Keywords: dimension measurement; laser triangulation; deep learning; calibration; key point detection; online measurement dimension measurement; laser triangulation; deep learning; calibration; key point detection; online measurement

Share and Cite

MDPI and ACS Style

Peng, T.; Zhang, Z.; Chen, F.; Zeng, D. Dimension Measurement and Key Point Detection of Boxes through Laser-Triangulation and Deep Learning-Based Techniques. Appl. Sci. 2020, 10, 26. https://doi.org/10.3390/app10010026

AMA Style

Peng T, Zhang Z, Chen F, Zeng D. Dimension Measurement and Key Point Detection of Boxes through Laser-Triangulation and Deep Learning-Based Techniques. Applied Sciences. 2020; 10(1):26. https://doi.org/10.3390/app10010026

Chicago/Turabian Style

Peng, Tao, Zhijiang Zhang, Fansheng Chen, and Dan Zeng. 2020. "Dimension Measurement and Key Point Detection of Boxes through Laser-Triangulation and Deep Learning-Based Techniques" Applied Sciences 10, no. 1: 26. https://doi.org/10.3390/app10010026

APA Style

Peng, T., Zhang, Z., Chen, F., & Zeng, D. (2020). Dimension Measurement and Key Point Detection of Boxes through Laser-Triangulation and Deep Learning-Based Techniques. Applied Sciences, 10(1), 26. https://doi.org/10.3390/app10010026

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop