Erratum to: Nondestructive Estimation of Lean Meat Yield of South
                    Korean Pig Carcasses Using Machine Vision Technique

Lohumi, Santosh; Wakholi, Collins; Baek, Jong Ho; Kim, Byeoung Do; Kang, Se Joo; Kim, Hak Sung; Yun, Yeong Kwon; Lee, Wang Yeol; Yoon, Sung Ho; Cho, Byoung‐Kwan

doi:10.5851/kosfa.2019.e47

Cited by 1 publication

(2 citation statements)

References 11 publications

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“…In this step, the objective is to skillfully slice open the porcine belly to facilitate subsequent processing of its contents. For unmanned and intelligent production, machine vision is commonly used in meat segmentation (Lohumi et al , 2019; Nyalala et al , 2021; Shi et al , 2021). But when machine vision is applied to the process of porcine belly cutting, there are also some shortcomings.…”

Section: Introductionmentioning

confidence: 99%

“…Machine vision is the most frequently used method in robotic control (Chen et al , 2022; Perez et al , 2016; Zhao et al , 2022b). Bar develops a prototype concept system to investigate the possibility of using robotics for the posttrimming of salmon fillets (Lohumi et al , 2019). Although machine vision and biometrics are used, paths are planned according to feature points during the cutting process and are not optimized.…”

Section: Introductionmentioning

confidence: 99%

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Online path planning of pork cutting robot using 3D laser point cloud

Liu,

Ning,

et al. 2024

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Purpose The purpose of this paper is to propose an new online path planning method for porcine belly cutting. With the proliferation in demand for the automatic systems of pork production, the development of efficient and robust meat cutting algorithms are hot issues. The uncertain and dynamic nature of the online porcine belly cutting imposes a challenge for the robot to identify and cut efficiently and accurately. Based on the above challenges, an online porcine belly cutting method using 3D laser point cloud is proposed. Design/methodology/approach The robotic cutting system is composed of an industrial robotic manipulator, customized tools, a laser sensor and a PC. Findings Analysis of experimental results shows that by comparing with machine vision, laser sensor-based robot cutting has more advantages, and it can handle different carcass sizes. Originality/value An image pyramid method is used for dimensionality reduction of the 3D laser point cloud. From a detailed analysis of the outward and inward cutting errors, the outward cutting error is the limiting condition for reducing the segments by segmentation algorithm.

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Section: Introductionmentioning

confidence: 99%