Lean Training Data Generation for Planar Object Detection Models in Unsteady Logistics Contexts

Dörr, Laura; Brandt, Felix; Meyer, Anne Marie; Pouls, Martin

doi:10.1109/icmla.2019.00062

Cited by 6 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dörr et al [Dör+19] present different approaches to generate a targeted dataset for logistics transport label detection. They take images of load carriers in realistic environments, where the load carriers have a colorful and easy-tosegment sheet of paper, where they would usually have a transport label.…”

Section: Label Recognitionmentioning

confidence: 99%

See 1 more Smart Citation

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)

View full text Add to dashboard Cite

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.

show abstract

Section: Label Recognitionmentioning

confidence: 99%

“…For a more in-depth literature review of barcode detection, we refer to the survey of Wudhikarn et al [WCM22]. In addition, the detection of dangerous goods labels [BBS21], transport label detection [Dör+19] and container OCR [She+] have been tackled.…”

Section: Label Recognitionmentioning

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)

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

On Scene Engineering and Domain Randomization: Synthetic Data for Industrial Item Picking

Weisenböhler

Hein

Wurll

2023

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Lean Training Data Generation for Planar Object Detection Models in Unsteady Logistics Contexts

Cited by 6 publications

References 10 publications

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

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

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

On Scene Engineering and Domain Randomization: Synthetic Data for Industrial Item Picking

Contact Info

Product

Resources

About