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Wang, P., Lin, Y., Muroiwa, R., Pike, S. and Mihaylova, L. (2019) Computer Vision Methods for Automating High Temperature Steel Section Sizing in Thermal Images. 2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF), Bonn, 15-17 October 2019, 1-6.
https://doi.org/10.1109/SDF.2019.8916635

has been cited by the following article:

• TITLE: Sectional Dimensions Identification of Metal Profile by Image Processing

  AUTHORS: İlhami M. Orak, Şaban Şeker

  KEYWORDS: Image Processing, Image Recognition, Profile, Section Measurement, Straight Lines Geometry

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.8, August 25, 2023

  ABSTRACT: 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.