Application of open Source Deep Neural Networks for Object Detection in Industrial Environments

Poss, Christian; Ibragimov, Olim; Indreswaran, Anoshan; Gutsche, Nils; Irrenhauser, Thomas; Prueglmeier, Marco; Goehring, Daniel

doi:10.1109/icmla.2018.00041

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…So specifically, the application of deep learning at container terminals can be further subdivided into two categories. One is the traditional application of deep learning for the scenario of container terminals, such as text recognition [ 21 , 22 ] and object detection [ 23 , 24 ]. Another is the deep learning for the scheduling and decision support in CTHS [ 25 ], such as container relocation problem [ 26 ] and container premarshalling problem [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

An Attention Mechanism Oriented Hybrid CNN‐RNN Deep Learning Architecture of Container Terminal Liner Handling Conditions Prediction

2021

Computational Intelligence and Neuroscience

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The booming computational thinking and deep learning make it possible to construct agile, efficient, and robust deep learning-driven decision-making support engine for the operation of container terminal handling systems (CTHSs). Within the conceptual framework of computational logistics, an attention mechanism oriented hybrid convolutional neural network and recurrent neural network deep learning architecture (AMO-HCR-DLA) is proposed technically to predict the container terminal liner handling conditions that mainly include liner handling time (LHT) and total working time of quay crane farm (TWT-QCF) for a calling liner. Consequently, the container terminal oriented logistics generalized computation (CTO-LGC) automation and intelligence are established tentatively by AMO-HCR-DLA. A typical regional container terminal hub of China is selected to design, implement, execute, and evaluate the AMO-HCR-DLA with the actual production data. In the case of severe vibration of LHT and TWT-QCF, while forecasting the handling conditions of 210 ships based on the CTO-LGC running log of four years, the forecasting error of LHT within one hour is more than 97% and that of TWT-QCF within six hours accounts for 89.405%. When predicting the operating conditions of 300 liners by the log of five years, the forecasting deviation of LHT within one hour is more than striking 99% and that of TWT-QCF within six hours reaches up to 94.010% as well. All are far superior to the predicting outcomes by the classical algorithms of machine learning and deep learning. Hence, the AMO-HCR-DLA shows excellent performance for the prediction of CTHS with the low and stable computational consuming. It also demonstrates the feasibility, credibility, and realizability of the computing architecture and design paradigm of AMO-HCR-DLA preliminarily.

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Section: Introductionmentioning

confidence: 99%

An Attention Mechanism Oriented Hybrid CNN‐RNN Deep Learning Architecture of Container Terminal Liner Handling Conditions Prediction

2021

Computational Intelligence and Neuroscience

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“…Poss et al (2018) untersuchen verschiedene Behältervarianten in zwei BMW-Werken. Dabei erfolgt keine Behältervereinzelung, sondern vielmehr die Erkennung von Behältern auf Paletten oder auf Bildern mit mehreren unterschiedlichen Behältertypen.…”

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Computer-Vision-Systeme im Behältermanagement

Dobhan¹,

Rein²,

Goletz³

et al. 2021

Mobility in a Globalised World 2020

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Dem Management von Mehrwegbehältern kommt in der nachhaltigen Logistik eine zentrale Bedeutung zu. Gleichzeitig sorgen Inlays und steigenden Behälteranforderungen durch die Robotik für Variantenreichtum hinsichtlich der Behälter. Die Digitalisierung und teilweise Automatisierung kann, die Effizienz und die Transparenz des Behältermanagements steigern. Ziel dieses Beitrags ist es, mit einer entsprechenden Studie in einer Laborumgebung zu zeigen, wie Computer-Vision-Systeme zur Digitalisierung im Behältermanagement beitragen können.

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Object Detection for Human–Robot Interaction and Worker Assistance Systems

Tavakoli,

Suh,

Walunj

et al. 2023

Artificial Intelligence in Manufacturing

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The primary goal of this research is to describe the scenarios, challenges, and complexities associated with object detection in industrial environments and to provide clues on how to tackle them. While object detection in production lines offers significant advantages, it also poses notable difficulties. This chapter delves into the common scenarios and specific challenges encountered in industrial object detection and proposes targeted solutions for various use cases. For example, synthetic data play a pivotal role in overcoming labeling challenges, particularly when it comes to small objects. By harnessing synthetic data, we can efficiently track and debug object detection results, ensuring faster identification and resolution of many data labeling issues. Synthetic data facilitate effective tracking and debugging of object detection results, streamlining the overall workflow. Furthermore, we explore the application of object detection in head-worn devices, utilizing the human point of view (POV) as a valuable perspective. This approach not only enhances human assistance systems but also enhances safety in specific use cases. Through this research endeavor, our aim is to contribute to the advancement of the whole process of object detection methods in complex industrial environments.

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Application of open Source Deep Neural Networks for Object Detection in Industrial Environments

Cited by 5 publications

References 4 publications

An Attention Mechanism Oriented Hybrid CNN‐RNN Deep Learning Architecture of Container Terminal Liner Handling Conditions Prediction

An Attention Mechanism Oriented Hybrid CNN‐RNN Deep Learning Architecture of Container Terminal Liner Handling Conditions Prediction

Computer-Vision-Systeme im Behältermanagement

Object Detection for Human–Robot Interaction and Worker Assistance Systems

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