Application of Improved Yolov3 for Pill Manufacturing System

Pill defects encountered during the manufacturing process may cause in low quality product and high timeline delays, and costs. In this paper, an improved convolutional neural network is proposed for automatic pill defects detection during pill manufacturing. In the first step, Gauss filtering and smoothing techniques is implemented for complex background-weakening purpose. Then, Hog feature extraction is executed to simplify the representation of the image that contains only the most important information about the image. The aim of this sub-process is to reduce the computation burden. Lastly, an improved YOLO model is proposed for online detection of pill defects and it was validated on our experiment platform in the laboratory for online pill defect detection. The proposed approach obtains robust quantification of internal pill cracks. This proposed approach is effective tool implemented into the industrial pill manufacturing system. Copyright (C) 2021 The Authors.