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. (https://bhban.kr/data)

翻译：我们提出了“CongNaMul”数据集，这是一个为豆芽图像分析中多种任务设计的综合性数据集。该数据集旨在支持图像分类、语义分割、分解以及长度和重量测量等任务。分类任务提供了四个类别以判定豆芽质量：正常、断裂、斑点、断裂且斑点，用于开发基于人工智能的自动质量检测技术。对于语义分割，数据集包含从单颗豆芽图像到多颗豆芽图像等不同复杂度的图像，并配有经过人工标注的掩码图像。标签包含4个不同类别：背景、豆头、豆身、豆尾。数据集还为图像分解任务提供了图像和掩码，包括两颗分离的豆芽图像及其合并形式。最后，提供了豆芽的5种物理特征（豆头长度、豆身长度、豆身厚度、豆尾长度、重量），用于基于图像的测量任务。该数据集有望成为豆芽图像高级分析领域广泛研究和应用的宝贵资源。同时，我们希望该数据集能帮助其他工业领域研究分类、语义分割、分解及物理特征测量的研究人员评估其模型。数据集可从作者仓库获取。(https://bhban.kr/data)