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

Int J Adv Manuf Technol

2024

DOI: 10.1007/s00170-024-13874-4

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

Mohammad Shahin,

Mazdak Maghanaki,

Ali Hosseinzadeh

et al.

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

Gąsienica-Józkowy,

Cyganek,

Knapik

et al. 2024

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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.

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

Gąsienica-Józkowy,

Cyganek,

Knapik

et al. 2024

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

Cited by 1 publication

References 336 publications

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

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

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