软胶囊缺陷检测与丸形分级的最小二乘椭圆法

针对软胶囊实时缺陷检测与丸形分级问题，提出一种基于远心视觉的椭圆度测量方法及其系统。以最小二乘椭圆作为评定基准，用被测轮廓的最小外接椭圆和最大内接椭圆的通径定义椭圆度误差，建立了椭圆度评定的最小二乘数学模型；建立胶囊缺陷与丸形分级的远心视觉检测系统，给出胶囊缺陷与丸形分级的检测流程；以某药企的保健药丸为对象，开展胶囊缺陷与丸形分级的视觉检测实验。结果表明：合理设定阈值，丸形分级检测的准确率可达100%；单颗胶囊的检测用时0.712 s。通过开发并行处理算法，缩短耗时、提高效率，该椭圆度误差评定方法及远心视觉系统可用于胶囊缺陷与丸形分级的实时检测。

Least Square Ellipse Method for Defect Detection and Shape Grading of Soft Capsule

To address the problem of real-time defect detection and shape grading of soft capsule, a telecentric vision based ellipse measurement method and its system were developed. An ellipse was obtained by the least square fitting of the pixel points of the capsule contour which was employed as the evaluation standard of ellipticity. Sequentially, the ellipticity was defined and the least square-based ellipticity evaluation model was established. The ellipticity was the difference between the diameter of the smallest circumscribed ellipse and that of the largest inscribed ellipse. Then, a telecentric vision inspection system for capsule defect and shape grading was established and the detection process was given. Finally, the experiments were carried out on the health products from a pharmaceutical company. The experimental results show that the accuracy of shape grading can reach 100% when the threshold is set reasonably and the detection time of a single capsule is about 0.712 s. The efficiency can be improved by developing parallel algorithm. The proposed ellipticity error evaluation method and the telecentric vision system can be utilized for capsule in line defect detection and shape grading.