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Updated: Jul 19, 2025

Fast Inspection of Quality of Indigo Naturalis by Multiple Light Scattering
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Liquid Content Detection In Transparent Containers: A Benchmark.

You Wu1, Hengzhou Ye1, Yaqing Yang1

  • 1Guangxi Key Laboratory of Embedded Technology and Intelligent Information Processing, College of Information Science and Engineering, Guilin University of Technology, Guilin 541006, China.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new dataset (LCDTC) for detecting liquid content in transparent containers. This advances computer vision for applications like service robots by estimating liquid presence and location.

Keywords:
LCDTC datasetbenchmarkliquid content estimationtransparent container detection

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Liquid detection in transparent containers is crucial for daily life applications.
  • Existing methods for transparent container analysis are limited, focusing on detection or complex height estimation.
  • There's a need for generalized computer vision solutions for liquid content detection in real-world scenarios.

Purpose of the Study:

  • To introduce the Liquid Content Detection in Transparent Containers (LCDTC) dataset.
  • To propose an innovative task combining transparent container detection and liquid content estimation.
  • To provide a foundation for developing advanced computer vision applications for liquid analysis.

Main Methods:

  • Development of the LCDTC dataset with 5916 annotated images.
  • Annotation includes axis-aligned bounding boxes for containers and liquid presence.
  • Creation of two baseline detectors (LCD-YOLOF and LCD-YOLOX) using identity-preserved human posture detectors.

Main Results:

  • The LCDTC dataset offers a novel approach to liquid content detection.
  • Baseline detectors demonstrate feasibility for the proposed task.
  • The dataset facilitates more informative computer vision analysis beyond simple container localization.

Conclusions:

  • The LCDTC dataset and baseline models pave the way for enhanced liquid content detection in transparent containers.
  • This work aims to stimulate further research in this challenging and practical computer vision task.
  • Potential applications span service robots, security, and industrial monitoring systems.