Computer Vision Based Conveyor Belt Congestion Recognition in Logistics Industrial Parks

Li, Yifan; Niu, Yingchun; Liu, Yang; Zheng, Li; Wang, Zichen; Zhe, Wenming

doi:10.1109/etfa45728.2021.9613245

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…The proposal analyses the characteristics of roller conveyor congestion and allows the factory to make the decisions. Our goal is inspired by [28], but considers a different application scenario and provides a completely different solution.…”

Section: Related Workmentioning

confidence: 99%

MOSPPA: monitoring system for palletised packaging recognition and tracking

Castaño-Amorós

Fuentes²,

Gil³

2023

Int J Adv Manuf Technol

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The paper industry manufactures corrugated cardboard packaging, which is unassembled and stacked on pallets to be supplied to its customers. Human operators usually classify these pallets according to the physical features of the cardboard packaging. This process can be slow, causing congestion on the production line. To optimise the logistics of this process, we propose a visual recognition and tracking pipeline that monitors the palletised packaging while it is moving inside the factory on roller conveyors. Our pipeline has a two-stage architecture composed of Convolutional Neural Networks, one for oriented pallet detection and recognition, and another with which to track identified pallets. We carried out an extensive study using different methods for the pallet detection and tracking tasks and discovered that the oriented object detection approach was the most suitable. Our proposal recognises and tracks different configurations and visual appearance of palletised packaging, providing statistical data in real time with which to assist human operators in decision-making. We tested the precision-performance of the system at the Smurfit Kappa facilities. Our proposal attained an Average Precision (AP) of 0.93 at 14 Frames Per Second (FPS), losing only 1% of detections. Our system is, therefore, able to optimise and speed up the process of logistic distribution.

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Section: Related Workmentioning

confidence: 99%

MOSPPA: monitoring system for palletised packaging recognition and tracking

Castaño-Amorós

Fuentes²,

Gil³

2023

Int J Adv Manuf Technol

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“…Finally, Li et al [Li+21] present an approach to recognize congestions on conveyor belts. They use preprocessing steps in order to normalize the image of the observed area and subsequently employ edge detection techniques.…”

Section: Completeness and Occupancymentioning

confidence: 99%

“…Summary: Occupancy has been analyzed for forklift masts [ ÖAN16] and conveyor belts [Li+21], and completeness checks have been investigated for pallets [Dör+20; Dör+21] and order picking [Li+12].…”

Section: Completeness and Occupancymentioning

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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