用深度学习实现流水线上草莓成熟度自动分级，提升工业质检效率。
The RaspGrade Dataset: Towards Automatic Raspberry Ripeness Grading with Deep Learning
- 构建专用数据集RaspGrade，支持五级成熟度精准标注。
- 实例分割可准确提取单颗草莓轮廓，但颜色相近类难区分。
- 适合农业智能检测、食品工业自动化领域研究者参考。
本研究探索计算机视觉在快速、精准、无损食品质量评估中的应用，聚焦于工业流水线上实时将草莓分为五个成熟等级的挑战。为此，我们采集并精心标注了名为RaspGrade的专用草莓数据集。实例分割实验表明，可获得精确的果实级掩码；然而，由于颜色相似性和遮挡问题，部分成熟度等级分类困难，而其他等级则因颜色差异更易识别。RaspGrade数据集已公开于Hugging Face：https://huggingface.co/datasets/FBK-TeV/RaspGrade。
原文摘要 · Abstract (English)
This research investigates the application of computer vision for rapid, accurate, and non-invasive food quality assessment, focusing on the novel challenge of real-time raspberry grading into five distinct classes within an industrial environment as the fruits move along a conveyor belt. To address this, a dedicated dataset of raspberries, namely RaspGrade, was acquired and meticulously annotated. Instance segmentation experiments revealed that accurate fruit-level masks can be obtained; however, the classification of certain raspberry grades presents challenges due to color similarities and occlusion, while others are more readily distinguishable based on color. The acquired and annotated RaspGrade dataset is accessible on Hugging Face at: https://huggingface.co/datasets/FBK-TeV/RaspGrade.
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