Research on Intelligent Detection Technology of Transparent Liquid based on Style Transfer

Li, Xianhuai; Zhao, Chunming; Chen, Yong; Su, Yi; Li, Linfeng; Han, Genwei

doi:10.1109/bigdia56350.2022.9874025

Cited by 1 publication

(2 citation statements)

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“…Third, vision-based methods can be highly automated, reducing the need for human intervention and increasing efficiency. For instance, Li et al [ 23 ] proposed to combine the style transfer method with a segmentation network [ 24 ] to sense transparent liquids (such as water) in transparent containers. It can accurately identify the transparent liquid in the container and then provide assistance in estimating the liquid level height and volume of the transparent liquid.…”

Section: Related Workmentioning

confidence: 99%

“…There has been a lot of work dedicated to this problem before. For instance, Li et al [ 23 ] proposed to combine the style transfer method with a segmentation network [ 24 ] to sense transparent liquids (such as water) in transparent containers. The application of contrastive learning to convert a transparent liquid image into a colorful liquid image enables the perception of clear liquids without requiring further operations, thus easing the constraints on the operational domain.…”

Section: Introductionmentioning

confidence: 99%

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Liquid Content Detection In Transparent Containers: A Benchmark

Yang

et al. 2023

Sensors

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Various substances that possess liquid states include drinking water, various types of fuel, pharmaceuticals, and chemicals, which are indispensable in our daily lives. There are numerous real-world applications for liquid content detection in transparent containers, for example, service robots, pouring robots, security checks, industrial observation systems, etc. However, the majority of the existing methods either concentrate on transparent container detection or liquid height estimation; the former provides very limited information for more advanced computer vision tasks, whereas the latter is too demanding to generalize to open-world applications. In this paper, we propose a dataset for detecting liquid content in transparent containers (LCDTC), which presents an innovative task involving transparent container detection and liquid content estimation. The primary objective of this task is to obtain more information beyond the location of the container by additionally providing certain liquid content information which is easy to achieve with computer vision methods in various open-world applications. This task has potential applications in service robots, waste classification, security checks, and so on. The presented LCDTC dataset comprises 5916 images that have been extensively annotated through axis-aligned bounding boxes. We develop two baseline detectors, termed LCD-YOLOF and LCD-YOLOX, for the proposed dataset, based on two identity-preserved human posture detectors, i.e., IPH-YOLOF and IPH-YOLOX. By releasing LCDTC, we intend to stimulate more future works into the detection of liquid content in transparent containers and bring more focus to this challenging task.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%