Summary of Liqd: a Dynamic Liquid Level Detection Model Under Tricky Small Containers, by Yukun Ma et al.
LiqD: A Dynamic Liquid Level Detection Model under Tricky Small Containers
by Yukun Ma, Zikun Mao
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed container dynamic liquid level detection model based on U^2-Net uses a combination of techniques to accurately detect changes in liquid levels. The model first generates an initial dataset using the SAM model, then filters out high-quality pseudo-label images through the SemiReward framework to build an exclusive dataset. This dataset is used to train the U^2-Net model, which extracts mask images of containers and compensates for mask defects through morphological processing. The model then classifies the liquid level state by calculating the grayscale difference between adjacent video frame images at the same position, segmenting the liquid level change area, and using a lightweight neural network. This approach is robust and versatile, reducing the demand for training data while mitigating the impact of intricate surroundings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to detect changes in liquid levels inside containers without touching them. The method uses a special kind of AI called U^2-Net, which can learn from small amounts of data. First, it creates a dataset by looking at how things look from different angles. Then, it gets rid of bad images and trains the model on the good ones. Next, it uses the trained model to analyze videos taken from cameras placed around the container. It does this by comparing each frame of video to the one before it and finding where the liquid level is changing. This approach works well even when there are lots of distractions in the background, making it a reliable solution for detecting changes in liquid levels. |
Keywords
» Artificial intelligence » Mask » Neural network » Sam
