We present 'CongNaMul', a comprehensive dataset designed for various tasks in
soybean sprouts image analysis. The CongNaMul dataset is curated to facilitate
tasks such as image classification, semantic segmentation, decomposition, and
measurement of length and weight. The classification task provides four classes
to determine the quality of soybean sprouts: normal, broken, spotted, and
broken and spotted, for the development of AI-aided automatic quality
inspection technology. For semantic segmentation, images with varying
complexity, from single sprout images to images with multiple sprouts, along
with human-labelled mask images, are included. The label has 4 different
classes: background, head, body, tail. The dataset also provides images and
masks for the image decomposition task, including two separate sprout images
and their combined form. Lastly, 5 physical features of sprouts (head length,
body length, body thickness, tail length, weight) are provided for image-based
measurement tasks. This dataset is expected to be a valuable resource for a
wide range of research and applications in the advanced analysis of images of
soybean sprouts. Also, we hope that this dataset can assist researchers
studying classification, semantic segmentation, decomposition, and physical
feature measurement in other industrial fields, in evaluating their models. The
dataset is available at the authors' repository. (this https URL)

我们呈现了一个名为 'CongNaMul' 的综合数据集，旨在进行大豆芽图像分析的各种任务。该数据集包含用于图像分类、语义分割、分解和长度重量测量等任务的图像和标签，为 AI 辅助自动质量检测技术的开发提供了四个用于确定大豆芽质量的类别，以及包含各种复杂度的图像和标签，等等。此外，数据集还提供了大豆芽图像分解和基于图像的物理特征测量任务所需的图像和标签。该数据集预计将成为图像高级分析领域中各种研究和应用的宝贵资源，并希望能够协助其他产业领域对分类、语义分割、分解和物理特征测量进行研究的研究人员评估其模型。该数据集可在作者的存储库（链接：https:// 此处为 URL）上获得。