Visual quality inspection of capsule heads utilizing shape and gray information

Abstract

Capsule quality inspection is important and necessary in the pharmaceutical industry. The popular methods often mis-detect capsule head defects. To solve this problem, we propose a high-quality visual defect inspection method for capsule heads. In detail, first, capsule head images are captured by high-speed cameras with ring illuminators. Then, radial symmetry transform (RST) is employed to locate region of interest (ROI). Next, the ROI image is enhanced by homomorphic filter and binarized by basic global thresholding. After that, six discriminative features of ROI are extracted, which are skeleton feature, binary density, number of connected boundaries, RST power, mean, and variance. Finally, these features are classified by support vector machine to inspect the quality of the capsule head. The experiment is carried out on a self-established capsule image database, Northeastern University Capsule Image Database Version 1.0. According to our experiment, the proposed method can detect ROI correctly for all of the capsule head images and inspection accuracy achieves a true positive rate of 100.00% and true negative rate of 100.00%.