构建1034张食品图像数据集，助力零售智能识别
Creation and Evaluation of a Food Product Image Dataset for Product Property Extraction
- 基于GS1标准采集并标注食品图像，含5类标签与30个检测框
- 数据集包含1034张高质量单件食品图像，适用于识别分类任务
- 适合做零售自动化、计算机视觉的模型开发者参考使用
人工智能的快速发展使零售企业能够自动化流程并降低成本，许多基于机器学习和计算机视觉的自动化方法依赖高质量训练数据。本文描述了一个标注数据集的创建过程，该数据集包含1,034张在影棚条件下拍摄的单件食品图像，标注了5类类别标签和30个目标检测标签，可用于产品识别与分类任务。所有图像与标签均依据全球非营利组织GS1的标准制定。本研究旨在支持零售领域机器学习模型的开发，并提供创建必要训练数据的参考流程。
原文摘要 · Abstract (English)
The enormous progress in the field of artificial intelligence (AI) enables retail companies to automate their processes and thus to save costs. Thereby, many AI-based automation approaches are based on machine learning and computer vision. The realization of such approaches requires high-quality training data. In this paper, we describe the creation process of an annotated dataset that contains 1,034 images of single food products, taken under studio conditions, annotated with 5 class labels and 30 object detection labels, which can be used for product recognition and classification tasks. We based all images and labels on standards presented by GS1, a global non-profit organisation. The objective of our work is to support the development of machine learning models in the retail domain and to provide a reference process for creating the necessary training data.
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