Surface Quality Automatic Inspection for Pharmaceutical Capsules Using Deep Learning

Abstract

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.