Minimum elastic bounding box algorithm for dimension detection of 3D objects: a case of airline baggage measurement

Gao, Qingji; Yin, Da‐Chuan; Luo, Qijun; Liu, Jingbin

doi:10.1049/iet-ipr.2017.0695

Cited by 7 publications

(2 citation statements)

References 28 publications

(56 reference statements)

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“…Box dimension measurement is meaningful in the logistics industry and has gradually attracted the attention of researchers. In industrial applications, various computer vision methods, such as stereo vision [1,2], deep camera [3][4][5][6], and structured light [7,8], have been developed for 3D information measurement. Two effective approaches [5,6] automatically detect box objects and estimate their dimensions by using depth cameras based on TOF technology, with an average error of 8 and 5 mm, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Peng et al [7] presented a box dimension measurement system based on multiline structure light vision, with errors of less than 5 mm. Gao et al [8] developed an airline baggage dimension detection approach using a 3D point cloud obtained through a 2D laser rangefinder measurement system. Recently, line structure light detection technology is one of the most common methods for measuring the geometric parameters of objects and for 3D reconstruction.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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