Improving operations through a lean AI paradigm: a view to an AI-aided lean manufacturing via versatile convolutional neural network

Shahin, Mohammad; Maghanaki, Mazdak; Hosseinzadeh, Ali; Chen, F. Frank

doi:10.1007/s00170-024-13874-4

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Book2

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 5 publications

References 336 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Minimizing waste via novel fuzzy hybrid stacked ensemble of vision transformers and CNNs to detect defects in metal surfaces

Hosseinzadeh,

Shahin,

Maghanaki

et al. 2024

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Minimizing waste via novel fuzzy hybrid stacked ensemble of vision transformers and CNNs to detect defects in metal surfaces

Hosseinzadeh,

Shahin,

Maghanaki

et al. 2024

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Research progress on the artificial intelligence applications in food safety and quality management

Yu,

Ouyang,

Zhang

et al. 2024

Trends in Food Science & Technology

View full text Add to dashboard Cite

Deep Learning-Based Monocular Estimation of Distance and Height for Edge Devices

Gąsienica-Józkowy,

Cyganek,

Knapik

et al. 2024

Information

View full text Add to dashboard Cite

Accurately estimating the absolute distance and height of objects in open areas is quite challenging, especially when based solely on single images. In this paper, we tackle these issues and propose a new method that blends traditional computer vision techniques with advanced neural network-based solutions. Our approach combines object detection and segmentation, monocular depth estimation, and homography-based mapping to provide precise and efficient measurements of absolute height and distance. This solution is implemented on an edge device, allowing for real-time data processing using both visual and thermal data sources. Experimental tests on a height estimation dataset we created show an accuracy of 98.86%, confirming the effectiveness of our method.

show abstract

Improving operations through a lean AI paradigm: a view to an AI-aided lean manufacturing via versatile convolutional neural network

Cited by 5 publications

References 336 publications

Minimizing waste via novel fuzzy hybrid stacked ensemble of vision transformers and CNNs to detect defects in metal surfaces

Minimizing waste via novel fuzzy hybrid stacked ensemble of vision transformers and CNNs to detect defects in metal surfaces

Research progress on the artificial intelligence applications in food safety and quality management

Deep Learning-Based Monocular Estimation of Distance and Height for Edge Devices

Contact Info

Product

Resources

About