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Journal ArticleDOI

Bolt loosening angle detection based on binocular vision

Shixu Wang, +4 more
- 11 Nov 2022 - 
- Vol. 34, Iss: 3, pp 035401-035401
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TLDR
In this article , a novel bolt loosening angle detection method based on binocular vision is presented. But, the method is prone to error scaused by the camera perspective.
Abstract
Bolt looseness detection is critical in preventing bolt connection failure. Compared to traditional sensor-based bolt looseness detection, image-based methods are low-cost and contactless and have thus become the highlight of research. However, current monocular vision-based detection methods are prone to error scaused by the camera perspective . In this paper, we present a novel bolt loosening angle detection method based on binocular vision. Key points on the bolt are detected and matched by SuperPoint Gauss network for 3D coordinates reconstruction and motion tracking. The bolt loosening angle is solved by fitting the rotation equation using random sample consensus. Experiments verify the proposed method performs well under different perspectives of camera and illumination conditions with an average error of 1.5°. Comparative test shows our method is superior to the monocular vision-based method in terms of accuracy when there is a large perspective angle. The proposed method is mark-free and robust to various working conditions, which makes it of great value for engineering application.

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Journal ArticleDOI

Lightweight wood panel defect detection method incorporating attention mechanism and feature fusion network

Yongxin Cao, +1 more
- 21 Jun 2023 - 
TL;DR: Wang et al. as discussed by the authors proposed a lightweight wood panel defect detection method called YOLOv5-LW, which incorporates attention mechanisms and a feature fusion network. But this method is not suitable for high dimensional wood panels.
References
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Proceedings Article

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Journal ArticleDOI

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

TL;DR: New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.
Journal ArticleDOI

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