基于深度学习的药物胶囊表面质量自动检测

J. Sensors Pub Date : 2022-08-26 DOI:10.1155/2022/4820618
Hao Dong, Jing Yang, Jun Wang, Shaobo Li
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引用次数: 2

摘要

胶囊通常用作大多数药品的容器。因此,胶囊的质量与产品的治疗效果和患者的健康密切相关。目前,在药用胶囊的实际生产中,表面质量检测是一项必不可少的工作。本研究提出了一种基于深度学习的胶囊缺陷检测模型,称为CapsuleDet,用于对胶囊生产过程中图像传感器数据中的缺陷进行分类和定位,以供实际应用。采用基于引导滤波的图像增强方法和混合数据增强方法分别提高原始数据的质量和数量,以缓解低对比度问题和增强模型训练的鲁棒性。利用可变形卷积模块和注意融合特征金字塔,有效利用提取的特征图中的语义和几何信息,适应不同形状和尺度缺陷的检测,提高了包膜缺陷的检测效果。在胶囊缺陷数据集上的评价结果表明,该方法的平均精度为92.91%,帧速率为22.16帧/秒。而且,它在训练时间、模型大小、检测精度、速度等方面的综合性能都优于目前流行的检测器。
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Surface Quality Automatic Inspection for Pharmaceutical Capsules Using Deep Learning
Capsules are commonly used as containers for most pharmaceutical products. Thus, the quality of a capsule is closely related to the therapeutic effect of the products and patient health. At present, surface quality testing is an essential task in the actual production of pharmaceutical capsules. In this study, a deep learning-based capsule defect detection model, called CapsuleDet, is proposed to classify and localize defects in image sensor data from capsule production for practical application. A guided filter-based image enhancement method and hybrid data augmentation method are used in improving the quality and quantity of the raw data, respectively, to mitigate the low contrast issue and enhance the robustness of the model training. Deformable convolution module and attentional fusion feature pyramid are also used to improve the detection effect of capsule defects by effectively utilizing the semantic and geometric information in the extracted feature maps and catering to the detection of defects with different shapes and scales. The evaluation results on the capsule defect dataset demonstrate that the proposed method achieves 92.91% mean average precision and 22.16 frames per second. Moreover, its overall performance in terms of training time, model size, detection accuracy, and speed is better than that of the currently popular detectors.
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