A transparent container liquid content detection method based on I-YOLOF

By designing the I-YOLOF model, the problems of cumbersome detection process and liquid content estimation for transparent containers were solved, enabling fast and accurate identification of liquid content in transparent containers and improving the real-time performance and accuracy of detection.

CN116844139BActive Publication Date: 2025-11-07GUILIN UNIVERSITY OF TECHNOLOGY
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Patent Information

Application Number
CN202310626045.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-05-31
Publication Date
2025-11-07
Estimated Expiration
2043-05-31

AI Technical Summary

Technical Problem

Existing methods for testing transparent containers are cumbersome, making it difficult to meet the requirements for real-time performance and accuracy. Furthermore, the process of estimating liquid content is tedious and lacks practicality.

Method used

The I-YOLOF model was designed and trained to identify transparent containers and their liquid content categories. The decoder of the I-YOLOF model contains three parallel head branches for regression localization, container classification, and liquid content prediction. The loss function was optimized and trained by combining a triplet attention module.

Benefits of technology

This invention enables a rapid and accurate method for identifying the liquid content in transparent containers, improving the real-time performance and accuracy of detection and expanding the application scenarios for transparent container detection.

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Abstract

The application discloses a kind of transparent container liquid content detection methods based on I-YOLOF, it is related to transparent container liquid content identification technical field.The picture of transparent container is collected, the picture is marked according to empty bottle, little water, half water, more water and full water 5 kinds of categories, and the collected data set is divided into training set and test set, I-YOLOF model is trained using training set, test set is used to test model effect, finally obtain the I-YOLOF model that can be used to detect the liquid content of transparent container.I-YOLOF network model is the variant of YOLOF, and the improvement method is: the main network uses ResNet50 to extract features;Decoder introduces a new head branch for predicting liquid content, which is parallel to the branch used for container classification, and a ternary attention module is used.The application can quickly and accurately identify the liquid content category in the transparent container by designing and training the I-YOLOF model based on detecting the transparent container.
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