FruitSeg30_细分数据集和掩码注释:用于多种水果细分和分类的新型数据集.
F M Javed Mehedi Shamrat1, Rashiduzzaman Shakil2, Mohd Yamani Idna Idris1
1Department of Computer System and Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia.
Data in brief
|September 10, 2024
概括
一个新的数据集,FruitSeg30,有助于果实细分和分类的深度学习. 在此数据集上训练的模型显示出高准确性,改善了农业技术和食品工业应用.
科学领域:
- 计算机视觉 计算机视觉
- 农业技术 农业技术
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 果实对人类营养至关重要,需要高效的农业工艺.
- 准确的水果分类和细分对于自动分类和质量控制至关重要,从而降低成本并提高一致性.
- 现有的数据集可能缺乏水果分析中强大的深度学习模型所需的多样性和质量.
研究的目的:
- 推出FruitSeg30_Segmentation数据集和面具注释,这是一个用于水果细分和分类的新型数据集.
- 为训练高级深度学习模型提供多样化和高质量的图像集合.
- 建立农业应用数据集质量和多样性的新基准.
主要方法:
- 开发了FruitSeg30数据集,其中包含30个水果类别的1969张图像.
- 使用U-Net架构进行深度学习模型培训.
- 使用准确性,精度,回忆,F1分数,IoU和Dice分数等指标进行性能评估.
主要成果:
- 该U-Net模型实现了高性能指标:94.72%的训练准确度,92.57%的验证准确度,94%的精度,91%的回忆,92.5%的F1得分,86%的IOU和0.9472的子得分.
- 该数据集在细分任务中表现出卓越的性能.
- 结果表明,FruitSeg30数据集在提高水果细分能力方面的有效性.
结论:
- 果实Seg30数据集解决了水果图像分析资源的严重缺口.
- 该数据集增强了深度学习模型在农业技术和食品工业中的潜力.
- 这项工作为与水果相关的人工智能应用中的数据集质量和多样性设定了新的标准.
相关概念视频
01:58Fruit Development, Structure, and Function
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