{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T18:17:09Z","timestamp":1773166629739,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T00:00:00Z","timestamp":1568160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61572307"],"award-info":[{"award-number":["61572307"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Portable box volume measurement has always been a popular issue in the intelligent logistic industry. This work presents a portable system for box volume measurement that is based on line-structured light vision and deep learning. This system consists of a novel 2 \u00d7 2 laser line grid projector, a sensor, and software modules, with which only two laser-modulated images of boxes are required for volume measurement. For laser-modulated images, a novel end-to-end deep learning model is proposed by using an improved holistically nested edge detection network to extract edges. Furthermore, an automatic one-step calibration method for the line-structured light projector is designed for fast calibration. The experimental results show that the measuring range of our proposed system is 100\u20131800 mm, with errors less than \u00b15.0 mm. Theoretical analysis indicates that within the measuring range of the system, the measurement uncertainty of the measuring device is \u00b10.52 mm to \u00b14.0 mm, which is consistent with the experimental results. The device size is 140 mm \u00d7 35 mm \u00d7 35 mm and the weight is 110 g, thus the system is suitable for portable automatic box volume measurement.<\/jats:p>","DOI":"10.3390\/s19183921","type":"journal-article","created":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T11:26:34Z","timestamp":1568201194000},"page":"3921","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Portable System for Box Volume Measurement Based on Line-Structured Light Vision and Deep Learning"],"prefix":"10.3390","volume":"19","author":[{"given":"Tao","family":"Peng","sequence":"first","affiliation":[{"name":"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"}]},{"given":"Zhijiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"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"}]},{"given":"Yingjie","family":"Song","sequence":"additional","affiliation":[{"name":"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"}]},{"given":"Fansheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Infrared Perception, Chinese Academy of Sciences, Shanghai 200444, China"}]},{"given":"Dan","family":"Zeng","sequence":"additional","affiliation":[{"name":"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"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Park, H.M., Van Messemac, A., and De Neveac, W. 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