Data Science Team, Hyundai Mobis, Seoul 06141, Korea.
Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Korea.
Sensors (Basel). 2021 Jul 25;21(15):5039. doi: 10.3390/s21155039.
In this study, we present a framework for product quality inspection based on deep learning techniques. First, we categorize several deep learning models that can be applied to product inspection systems. In addition, we explain the steps for building a deep-learning-based inspection system in detail. Second, we address connection schemes that efficiently link deep learning models to product inspection systems. Finally, we propose an effective method that can maintain and enhance a product inspection system according to improvement goals of the existing product inspection systems. The proposed system is observed to possess good system maintenance and stability owing to the proposed methods. All the proposed methods are integrated into a unified framework and we provide detailed explanations of each proposed method. In order to verify the effectiveness of the proposed system, we compare and analyze the performance of the methods in various test scenarios. We expect that our study will provide useful guidelines to readers who desire to implement deep-learning-based systems for product inspection.
在本研究中，我们提出了一种基于深度学习技术的产品质量检测框架。首先，我们对可应用于产品检测系统的几种深度学习模型进行了分类。此外，我们详细解释了构建基于深度学习的检测系统的步骤。其次，我们解决了将深度学习模型有效链接到产品检测系统的连接方案。最后，我们提出了一种有效的方法，可以根据现有产品检测系统的改进目标来维护和增强产品检测系统。由于采用了所提出的方法，所提出的系统被观察到具有良好的系统维护和稳定性。所有提出的方法都集成到一个统一的框架中，我们对每个提出的方法都进行了详细的解释。为了验证所提出系统的有效性，我们在各种测试场景中比较和分析了方法的性能。我们希望本研究能为希望为产品检测实施基于深度学习的系统的读者提供有用的指导。
