Dataset of Packaging Defects and Associated Cost of Poor Quality in a Wine Bottling Process

This dataset contains 9,651 images acquired from a real wine bottling packaging process. The dataset is intended for applications in quality control, computer vision, and process improvement in industrial environments.
The images are organized into three defect classes: missing cap, missing bottle, and wrong bottle. Each image reflects actual production conditions, where bottles are arranged in boxes with a capacity of 12 units, consistent with the real packaging configuration.
To ensure variability and robustness in visual features, multiple cap colors are included, namely black, red, orange, blue, purple, green, and silver. This diversity supports the development of more generalizable defect detection and classification models.
The dataset was divided into training (70%), validation (20%), and testing (10%) subsets, facilitating its direct use in supervised learning tasks.
Additionally, the dataset includes a configuration file (data.yaml) that specifies the directory paths for each subset (training, validation, and testing) as well as the corresponding class labels. This file enables straightforward integration with deep learning frameworks, particularly those based on YOLO architectures.
This dataset can support research in defect detection, automated inspection systems, and quality improvement methodologies such as Six Sigma and Industry 4.0 applications.

本数据集包含9651张采集自真实葡萄酒灌装包装流程的图像，旨在应用于工业环境中的质量管控、计算机视觉及流程优化场景。
图像被划分为三类缺陷类别：缺盖、缺瓶与错瓶。每张图像均反映真实生产工况，酒瓶以单箱容量12瓶的形式排布于包装箱中，与实际包装配置完全一致。
为保障视觉特征的多样性与模型鲁棒性，数据集涵盖多种瓶盖颜色，具体包括黑色、红色、橙色、蓝色、紫色、绿色及银色。此类特征多样性有助于开发泛化性更强的缺陷检测与分类模型。
本数据集已划分为训练集（70%）、验证集（20%）与测试集（10%）三个子集，可直接用于监督学习任务。
此外，数据集包含配置文件（data.yaml），该文件指定了各子集（训练、验证、测试）的目录路径以及对应的类别标签，可便捷集成至深度学习框架，尤其是基于YOLO架构的框架。
本数据集可支撑缺陷检测、自动化检测系统以及六西格玛（Six Sigma）、工业4.0（Industry 4.0）等质量改进方法的相关研究。