Paper
7 August 2024 Liquid level detection technology based on degradation model restoration and edge detection technology
Yan Wang, Jinyan Pang
Author Affiliations +
Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 1322906 (2024) https://doi.org/10.1117/12.3038143
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China
Abstract
In the field of optical devices, numerous specialized materials exist, and conventional detection techniques are inadequate for determining the residual capacity of these materials during daily device usage. This uncertainty poses significant challenges to device utilization. Ascertaining the remaining amount of special material in a container is unfeasible, necessitating refilling before each use; however, opening the container causes evaporation of the special material, resulting in wastage of valuable resources. Due to its chemical properties, transparent containers cannot be used for storage or estimating remaining capacity through visual inspection. Therefore, employing X-ray imaging and computer vision-based image processing offers an effective solution to determine residual capacity by capturing images of metal containers and subsequently analyzing them using edge detection and image restoration technologies proposed in this study. This method successfully extracts the contour boundary of the special material in the original image, and provides necessary support for the subsequent calculation.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Yan Wang and Jinyan Pang "Liquid level detection technology based on degradation model restoration and edge detection technology", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 1322906 (7 August 2024); https://doi.org/10.1117/12.3038143
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KEYWORDS
Image processing

Edge detection

Image restoration

Image quality

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