Image Processing-Based Weight Estimation for Steel Cylinders

We have been tackling a problem of weight estimation of steel cylinders using image processing with an error of less than ±10g. In our previous studies, the weight was estimated by capturing multiple images of an entire side of the steel cylinder, extracting the contours via image processing, and then constructing a 3D model based on the obtained data. However, the estimation performance was poor. To improve accuracy, a simple cylinder model was considered in addition to the previous method, and multiple regression analysis was applied on the weights calculated from both models. As a result, high estimation performance was achieved. However, detailed analyses and discussions of these results were insufficient. Therefore, they are investigated in this study.

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Publication Details

Journal
IEEJ Journal of Industry Applications
Published
2024-08-15
DOI
https://doi.org/10.1541/ieejjia.24002682
Citations
2
Primary Topic
Vehicle License Plate Recognition
Type
article
Field-Weighted Citation Impact
0.64
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article

Image Processing-Based Weight Estimation for Steel Cylinders

Shota Asahi, Junya Sato, Chikara Ito, Kosei Umemura et al.
2 citations
IEEJ Journal of Industry Applications
Vehicle License Plate Recognition
0.64
article

Image Processing-Based Weight Estimation for Steel Cylinders

Shota Asahi, Junya Sato, Chikara Ito, Kosei Umemura, Hidenobu Masuda, Kaishi Shimizu, Takumi Hirashita
article en
2 citations

Abstract

We have been tackling a problem of weight estimation of steel cylinders using image processing with an error of less than ±10g. In our previous studies, the weight was estimated by capturing multiple images of an entire side of the steel cylinder, extracting the contours via image processing, and then constructing a 3D model based on the obtained data. However, the estimation performance was poor. To improve accuracy, a simple cylinder model was considered in addition to the previous method, and multiple regression analysis was applied on the weights calculated from both models. As a result, high estimation performance was achieved. However, detailed analyses and discussions of these results were insufficient. Therefore, they are investigated in this study.

IEEJ Journal of Industry ApplicationsVol. 13(6)
Gifu University (JP)
Openalex Percentile: Top 28%
Vehicle License Plate Recognition
0.64
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