Dimension Measurement and Key Point Detection of Boxes through Laser-Triangulation and Deep Learning-Based Techniques
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Optical measurement and interference techniques · 3D Surveying and Cultural Heritage · Image and Object Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
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
Key Laboratory of Intelligent Infrared Perception, Chinese Academy of Sciences, Shanghai 200444, China
Funding
National Natural Science Foundation of China
References (35)
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