Capsules are commonly used as containers for most pharmaceuticals, and capsule quality is closely related to human health. Given the actual demand for capsule production, this study proposes a capsule defect detection and recognition method based on an improved convolutional neural network (CNN) algorithm. The algorithm is used for defect detection and classification in capsule production. Defective and qualified capsule images in the actual production are collected as samples. Then, a deep learning model based on the improved CNN is designed to train and test a capsule image dataset and identify defective capsules. The improved CNN algorithm is based on regularization and the Adam optimizer (RACNN), on which a dropout layer and L2_regularization are added between the full connection and the output layer to solve the overfitting problem. The Adam optimizer is introduced to accelerate model training and improve model convergence. Then, cross entropy is used as a loss function to measure the prediction performance of the model. By comparing the results of RACNN with different parameters, a detection method based on the optimal parameters of the RACNN model is finally selected. Results show a 97.56% recognition accuracy of the proposed method. Hence, this method could be used for the automatic identification and classification of defective capsules.

中文翻译：

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基于改进的卷积神经网络的胶囊缺陷识别

胶囊通常用作大多数药物的容器，胶囊的质量与人体健康密切相关。鉴于胶囊生产的实际需求，本研究提出了一种基于改进的卷积神经网络（CNN）算法的胶囊缺陷检测与识别方法。该算法用于胶囊生产中的缺陷检测和分类。实际生产中有缺陷的合格胶囊图像将作为样本收集。然后，基于改进的CNN的深度学习模型被设计为训练和测试胶囊图像数据集并识别有缺陷的胶囊。改进的CNN算法基于正则化和Adam优化器（RACNN），在其全连接和输出层之间添加了辍学层和L2_regularization，以解决过拟合问题。引入了Adam优化器以加速模型训练并提高模型收敛性。然后，将交叉熵用作损失函数，以测量模型的预测性能。通过比较RACNN与不同参数的结果，最终选择了基于RACNN模型最优参数的检测方法。结果表明，该方法的识别精度为97.56％。因此，该方法可以用于缺陷胶囊的自动识别和分类。结果表明，该方法的识别精度为97.56％。因此，该方法可以用于缺陷胶囊的自动识别和分类。结果表明，该方法的识别精度为97.56％。因此，该方法可以用于缺陷胶囊的自动识别和分类。