Quality control is a crucial activity performed by manufacturing enterprises to ensure that their products meet quality standards and avoid potential damage to the brand's reputation. The decreased cost of sensors and connectivity enabled increasing digitalization of manufacturing. In addition, artificial intelligence enables higher degrees of automation, reducing overall costs and time required for defect inspection. This research compares three active learning approaches, having single and multiple oracles, to visual inspection. Six new metrics are proposed to assess the quality of calibration without the need for ground truth. Furthermore, this research explores whether existing calibrators can improve their performance by leveraging an approximate ground truth to enlarge the calibration set. The experiments were performed on real-world data provided by Philips Consumer Lifestyle BV. Our results show that the explored active learning settings can reduce the data labeling effort by between three and four percent without detriment to the overall quality goals, considering a threshold of p=0.95. Furthermore, the results show that the proposed calibration metrics successfully capture relevant information otherwise available to metrics used up to date only through ground truth data. Therefore, the proposed metrics can be used to estimate the quality of models' probability calibration without committing to a labeling effort to obtain ground truth data.
翻译:质量控制是制造企业的一项关键活动,其目的是确保其产品符合质量标准,避免对品牌声誉造成潜在损害。传感器和连通成本的降低使得制造业日益数字化。此外,人工智能还能够提高自动化程度,降低总体成本,缩短缺陷检查所需的时间。这项研究比较了三种积极的学习方法,即单一和多种神器,以视觉检查为特征。提出了六种新指标,用以评估校准质量,而不需要地面真相。此外,这项研究还探讨了现有校准者能否通过利用近似地面真相来扩大校准集来改进其性能。对菲利普消费者生活风格BV提供的实实在在世界数据进行了实验。我们的结果显示,探索的积极学习环境可以将标注数据减少3%至4%,而不会损害总体质量目标,同时考虑到p=0.95的阈值。此外,结果显示,拟议的校准指标成功地获取了相关的信息,而其他可用用于测量标准则只能通过地面真相数据来更新。因此,拟议的指标可以用来评估模型的概率校准质量,而不必承诺将数据标记为地面数据。