Estimation of Package-Boundary Confidence for Object Recognition in Rainbow-SKU Depalletizing Automation

Kento Sekiya, Taiki Yano, Nobutaka Kimura, Kiyoto Ito

2024

Abstract

We developed a reliable object recognition method for a rainbow-SKU depalletizing robot. Rainbow SKUs include various types of objects such as boxes, bags, and bottles. The objects’ areas need to be estimated in order to automate a depalletizing robot; however, it is difficult to detect the boundaries between adjacent objects. To solve this problem, we focus on the difference in the shape of the boundaries and propose package-boundary confidence, which assesses whether the recognized boundary correctly corresponds to that of an object unit. This method classifies recognition results into four categories on the basis of the objects’ shape and calculates the package-boundary confidence for each category. The results of our experimental evaluation indicate that the proposed method with slight displacement, which is automatic recovery, can achieve a recognition success rate of 99.0 %. This is higher than that with a conventional object recognition method. Furthermore, we verified that the proposed method is applicable to a real-world depalletizing robot by combining package-boundary confidence with automatic recovery.

Paper Citation

in Harvard Style

Sekiya K., Yano T., Kimura N. and Ito K. (2024). Estimation of Package-Boundary Confidence for Object Recognition in Rainbow-SKU Depalletizing Automation. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 309-316. DOI: 10.5220/0012307300003660

in Bibtex Style

@conference{visapp24,
author={Kento Sekiya and Taiki Yano and Nobutaka Kimura and Kiyoto Ito},
title={Estimation of Package-Boundary Confidence for Object Recognition in Rainbow-SKU Depalletizing Automation},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={309-316},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012307300003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Estimation of Package-Boundary Confidence for Object Recognition in Rainbow-SKU Depalletizing Automation
SN - 978-989-758-679-8
AU - Sekiya K.
AU - Yano T.
AU - Kimura N.
AU - Ito K.
PY - 2024
SP - 309
EP - 316
DO - 10.5220/0012307300003660
PB - SciTePress