We present a method for recognition of agglomerates in images acquired during the coating process of pharmaceutical pellets. The pellets in the images are not perfectly dispersed, and it is often hard to differentiate between a random group of primary particles and a real agglomerate. The method utilizes a clustering-based image segmentation for candidate region detection and a convolutional neural network for classification of detected pellets to primary particles or agglomerates. We validated the performance of the method on real images of pharmaceutical pellets acquired during the coating process and achieved 93% classification accuracy.

中文翻译：

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基于密度的聚类和卷积神经网络在线识别团聚的药丸

我们提出了一种在药物药丸包衣过程中获得的图像中识别附聚物的方法。图像中的颗粒没有完全分散，并且通常很难区分随机的一次颗粒组和真实的团块。该方法利用基于聚类的图像分割进行候选区域检测，并使用卷积神经网络将检测到的颗粒分类为初级颗粒或附聚物。我们在包衣过程中获得的药物颗粒的真实图像上验证了该方法的性能，并实现了93％的分类精度。