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RafanoSet:用于对象检测和细分的原始,手动和自动注释的Raphanus Raphanistrum杂草图像数据集

Shubham Rana¹, Salvatore Gerbino¹, Domenico Barretta¹

¹Department of Engineering, University of Campania "L. Vanvitelli", Via Roma 29, Aversa (CE) 81031, Italy.

|May 3, 2024

概括

这项研究引入了用于精准农业中杂草检测的新数据集. 它具有小麦田与野生卜的多谱图像,为计算机视觉任务提供手动和自动注释.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
遥感遥感遥感遥感

背景情况:

精准农业需要精确的杂草检测和细分,以有效地管理作物.
计算机视觉技术对于分析农业图像越来越重要.
现有的数据集可能缺乏高级杂草细分模型所需的详细注释.

研究的目的:

为准确农业中用于杂草检测和细分的新型,公开可用的数据集.
为了提供手动和自动注释的多光谱图像 * Triticum Aestivum * 与 * Raphanus raphanistrum * (野卜).
促进计算机视觉研究,用于在异质作物环境中识别和细分杂草.

主要方法:

精选了85个多光谱 (MS) 图像 (蓝色,绿色,红色,NIR,RedEdge) 的小麦田和野生卜.
使用VIA执行手动像素级注释,以COCO格式保存.
使用接地DINO + SAM在80张图像上以PASCAL VOC格式进行自动注释.
通过验证手动和自动注释以及可访问代码来确保数据质量.

主要成果:

一个包括85个MS图像的综合数据集,附有详细的像素级注释,用于杂草细分.
提供手动和自动注释集,经过质量验证.
该数据集包括对机器学习模型培训至关重要的光谱信息.

结论:

这一数据集是利用计算机视觉在精准农业中推进杂草检测和细分的宝贵资源.
它支持改进的图像注册和细分精度,特别是野生卜.
公开可用的数据和代码促进了农业人工智能的可复制性和进一步研究.

关键词:

自动注释自动注释接地DINO的接地多光谱的多光谱的分段任何模型模型.杂草细分杂草细分杂草细分

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