Artificial Neural Network Detection of Damaged Goods by Packaging State

Malyshev, M. I.; Braginsky, S.А.; Faddeeva, E.Yu.; Gogolin, S.S.

doi:10.1109/tirved53476.2021.9639113

Cited by 1 publication

(1 citation statement)

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“…Malyshev et al [Mal+21] present a concept outlining the use of CNNs for damage detection. They name deformation, rupture and moisture as possible categories of damage to a package.…”

Section: Damage and Temperingmentioning

confidence: 99%

Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection

Naumann

Dörr

Salscheider

et al. 2020

2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)

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Computer vision applications in transportation logisticsand warehousing have a huge potential for process automation. We present a structured literature review on research in the field to help leverage this potential. All literature is categorized w.r.t. the application, i.e. the task it tackles and w.r.t. the computer vision techniques that are used. Regarding applications, we subdivide the literature in two areas: Monitoring, i.e. observing and retrieving relevant information from the environment, and manipulation, where approaches are used to analyze and interact with the environment. In addition to that, we point out directions for future research and link to recent developments in computer vision that are suitable for application in logistics. Finally, we present an overview of existing datasets and industrial solutions. We conclude that while already many research areas have been investigated, there is still huge potential for future research. The results of our analysis are also available online at https://a-nau.github.io/cv-in-logistics.1 Verband der Automobilindustrie e.V. (VDA), see https://www.vda.de/en.

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“…Malyshev et al [Mal+21] present a concept outlining the use of CNNs for damage detection. They name deformation, rupture and moisture as possible categories of damage to a package.…”

Section: Damage and Temperingmentioning

confidence: 99%