Diaz Julio Zanon, Corcoran Peter
Electrical and Electronic Engineering, University of Galway, Galway, Ireland.
Data Brief. 2024 Oct 10;57:110996. doi: 10.1016/j.dib.2024.110996. eCollection 2024 Dec.
This manuscript delineates the constitution of a dataset engineered to bolster research endeavours in the realm of automated inspection of seals pertinent to Medical Device Packaging. The compendium encompasses a total of 1200 images of medical device pouches, with an equitable distribution between intact seals and those exhibiting defects. Each image boasts dimensions of 3008 by 4110 by 3 pixels. Accompanying the visual data is a suite of metadata essential for the importation of images into processing software. The defects within the dataset were meticulously crafted through a process of analysis and replication of imperfections commonly encountered on manufacturing lines, with the invaluable insights of seasoned inspectors and the support of Boston Scientific. The acquisition of images was conducted within a bespoke rig, meticulously designed to mirror the conditions prevalent in manufacturing environments. In addition to the imagery, the dataset contains binary labels and segmentation masks that delineate the defects, thereby facilitating the seamless utilization of the dataset for the training and validation of Machine Learning algorithms.
本手稿阐述了一个数据集的构成,该数据集旨在助力医疗器械包装密封件自动检测领域的研究工作。该汇编总共包含1200张医疗器械包装袋的图像,完好密封件和有缺陷密封件的图像数量均等。每张图像的尺寸为3008×4110×3像素。与视觉数据相伴的是一组元数据,这些元数据对于将图像导入处理软件至关重要。数据集中的缺陷是通过对生产线上常见瑕疵进行分析和复制的过程精心制作而成的,其中融入了经验丰富的检查员的宝贵见解,并得到了波士顿科学公司的支持。图像采集是在一个定制的装置内进行的,该装置经过精心设计以反映制造环境中普遍存在的条件。除了图像之外,数据集还包含二进制标签和分割掩码,用于描绘缺陷,从而便于将该数据集无缝用于机器学习算法的训练和验证。