Dual-modal edible oil impurity dataset for weak feature detection
Abstract Edible oil may be mixed with tiny solid impurities like raw material fragments, hair, metal fragments and etc. during the production and manufacturing process. For food safety reasons, these tiny impurities need to be detected in the quality control process. As compared with manual detectio...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-12-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04305-w |
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| Summary: | Abstract Edible oil may be mixed with tiny solid impurities like raw material fragments, hair, metal fragments and etc. during the production and manufacturing process. For food safety reasons, these tiny impurities need to be detected in the quality control process. As compared with manual detection ways, computer vision-based impurity detection methods can greatly reduce the workload and improve the detection efficiency and accuracy. However, there are currently no datasets for the detection of solid impurities in edible oils. For this reason, a dual-modal edible oil impurity (DMEOI) dataset is developed and made publicly available. The dataset is composed of 14520 event and full-pic images and annotated in two ways. Five common edible oils and four typical impurities are included in the dataset. The DMEOI dataset can be used for liquid impurity detection in either single-modal or dual-modal way. To prove the usability of the dataset, four object detection algorithms are applied and compared. |
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| ISSN: | 2052-4463 |