Visual detection and recognition for medical infusion automatic production lines
The detection of filling quality for pharmaceutical is an important element in pharmaceutical process,which guarantees the medication security.According to the detection demand of visible foreign substances for glass bottle medical infusion,a high speed automated foreign substance detection system based on the machine vision consisting of multi types image acquisition devices is developed.Firstly,a high speed high reliability preprocessing method for medical infusion images is proposed to eliminate the disturbance caused by mechanical vibration and tracking.Then,an improved fuzzy cellular neural network(IFCNN) is developed for image segmentation to effectively solve the problem of edge detection,which reveals the mechanism of foreign substances,particles as well as liquid bubbles,comprehensively analyzes the morphological boundary of the target operating characteristics,and obtains various characteristics and the dynamic change information in image sequences.Finally,the classification approaches of support vector machine(SVM) and AdaBoosting,based on image sequences target characteristics,is employed to recognize multiple tiny insoluble foreign substances.Experimental results indicate that the developed approach is efficient and increases the recognition rate,which is of great significance to the development of engineering equipment.