The pill manufacturing process accrues substantial financial costs due to quality. Pill quality inspection is laborious, time-consuming and subjective, resulting in poor statistical representation and inconsistent results. In this study, we developed an approach that integrates deep learning algorithms and computer-vision-based processing with an optimization algorithm to fully automate the image analysis of internal crack/contamination detection. This approach exploits the features learned by convolutional neural network using various sub-processing techniques and Adam optimization. It achieves robust quantification of internal pill defects with an average accuracy of 95%.

中文翻译：

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使用深度学习的自动药丸质量检查

由于质量，药丸制造过程会产生大量的财务成本。药丸质量检查费力、耗时且主观，导致统计表示不佳和结果不一致。在这项研究中，我们开发了一种将深度学习算法和基于计算机视觉的处理与优化算法相结合的方法，以完全自动化内部裂纹/污染检测的图像分析。这种方法利用卷积神经网络学习的特征，使用各种子处理技术和 Adam 优化。它以 95% 的平均准确度实现了对内部药丸缺陷的稳健量化。