Computer Vision Methods for Automating High Temperature Steel Section Sizing in Thermal Images

Wang, Peng; Lin, Yueda; Muroiwa, Ree; Pike, Simon; Mihaylova, Lyudmila

doi:10.1109/sdf.2019.8916635

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…When the registration process is finished, with the camera internal and external parameters obtained from the calibration process and based on the approach from [9], the steel section size can be calculated and converted from the image plane to the physical plane. In our case, two hot rolling bar (HRB) edges are expected to be within the sliding window I H×W , with height H and width W .…”

Section: Remote Sizing Of Hot Steel Sectionsmentioning

confidence: 99%

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Image Fusion for Remote Sizing of Hot High Quality Steel Sections

Lin

Wang

Muroiwa³

et al. 2021

Advances in Intelligent Systems and Computing

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This paper proposes an adaptive method for dual camera based steel section sizing, where high accuracy measuring is challenging due to the lack of well pronounced image features. The proposed approach includes additional information from a sidewise positioned checkerboard and enables adaptive image registration. A thorough evaluation of the registration results based on the virtual checkerboard is presented. On the accomplishment of image registration, both a fast Fourier transform and a discrete wavelet transform are adopted for fusion of the registered images. A range of comparisons with various metrics is conducted to achieve the best fusion quality. The hot steel section sizing results show an accuracy that is in line with the rolling standards, i.e. in the tolerance range less than 1.5 mm error.

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Section: Remote Sizing Of Hot Steel Sectionsmentioning

confidence: 99%

“…Our previous work [8,9] proposes a real-time measurement system based on monocular camera data. A fast structural random forest algorithm detects the steel bars' edges, and a regression algorithm extracts the edges in both optical and thermal videos.…”

Section: Introductionmentioning

confidence: 99%

Image Fusion for Remote Sizing of Hot High Quality Steel Sections

Lin

Wang

Muroiwa³

et al. 2021

Advances in Intelligent Systems and Computing

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“…Both HRB edges and environment edges are detected at this stage. Preliminary results with the structural random forests algorithm [7] and with thermal images are reported in our preceding paper [10]. The main idea is to detect edges in images via constructing a structured forest.…”

Section: A Edge Detectionmentioning

confidence: 99%

A Weighted Variance Approach for Uncertainty Quantification in High Quality Steel Rolling

Wang

Lin

Muroiwa³

et al. 2020

2020 IEEE 23rd International Conference on Information Fusion (FUSION)

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“…In dimensioning, extraction of the information from an image is as important as taking it. Wang et al carried out a study on estimating the dimensions by taking images with the help of a thermal camera and detecting the edges [4]. This method will be a more costly solution compared to other measurement systems.…”

Section: Introductionmentioning

confidence: 99%

Sectional Dimensions Identification of Metal Profile by Image Processing

Orak,

Şeker

2023

JCC

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In steel plants, estimation of the production system characteristic is highly critical to adjust the system parameters for best efficiency. Although the system parameters may be tuned very well, due to the machine and human factors involved in the production line some deficiencies may occur in product. It is important to detect such problems as early as possible. Surface defects and dimensional deviations are the most important quality problems. In this study, it is aimed to develop an approach to measure the dimensions of metal profiles by obtaining images of them. This will be of use in detecting the deviations in dimensions. A platform was introduced to simulate the real-time environment and images were taken from the metal profile using 4 laser light sources. The shape of the material is generated by combining the images taken from different cameras. Real dimensions were obtained by using image processing and mathematical conversion operations on the images. The results obtained with small deviations from the real values showed that this method can be applied in a real-time production line.

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Computer Vision Methods for Automating High Temperature Steel Section Sizing in Thermal Images

Abstract: This is a repository copy of Computer vision methods for automating high temperature steel section sizing in thermal images.

Cited by 4 publications

References 13 publications

Image Fusion for Remote Sizing of Hot High Quality Steel Sections

Image Fusion for Remote Sizing of Hot High Quality Steel Sections

A Weighted Variance Approach for Uncertainty Quantification in High Quality Steel Rolling

Sectional Dimensions Identification of Metal Profile by Image Processing

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