CAJ | 학술논문

针对我国医药生产检测包装线上大输液杂质智能检测技术问题,提出了一种利用实时视频图像处理技术检测识别大输液杂质的方法.该方法对连续多帧被旋转的大输液瓶图像运用差分图像运动分析方法提取目标杂质;运用图像处理技术去除气泡噪声,准确分割目标杂质,采用Camshift跟踪算法连续跟踪几帧运动杂质以确保检测准确率;根据Camshift跟踪算法提取出的杂质运动和几何特征,应用SVM(Support Vector Machine)准确识别杂质类型.实验结果表明,该方法检测识别直径大于等于4个像素的杂质的检测识别率平均可达到95.4%,检测识别速率平均可达到581 ms/瓶.
침대아국의약생산검측포장선상대수액잡질지능검측기술문제,제출료일충이용실시시빈도상처리기술검측식별대수액잡질적방법.해방법대련속다정피선전적대수액병도상운용차분도상운동분석방법제취목표잡질;운용도상처리기술거제기포조성,준학분할목표잡질,채용Camshift근종산법련속근종궤정운동잡질이학보검측준학솔;근거Camshift근종산법제취출적잡질운동화궤하특정,응용SVM(Support Vector Machine)준학식별잡질류형.실험결과표명,해방법검측식별직경대우등우4개상소적잡질적검측식별솔평균가체도95.4%,검측식별속솔평균가체도581 ms/병.
Considering that the intelligent detection and recognition of impurity in transfusion solution is a critically needed technology in pharmaceutical device production, a method for impurity detection and recognition using video image processing technology was proposed. Firstly, the impurity objects in several continuous rotated transfusion solution bottle frames were extracted cursorily by subtracting between two frames. Then, some image processing technologies were utilized to eliminate bubble noises and extract impurity objects accurately. And then, the Camshift tracing algorithm was adopted to trace the impurity to ensure the correct detection. Finally, Support Vector Machine was used to recognize the type of impurities with the extracted motion and geometrical features of impurities. Experiment results have shown that the mean detection and recognition rate is 95.4% for impurities equal and larger than four pixels in diameter. Mean detection and recognition time is 581ms for one transfusion solution bottle.