Research On The Key Technologies Of Target Tracking And Defect Detection For Pharmaceutical Packaging Inspection

AVI (Automated Visual Inspection) are now playing an important role in contemporary industrial manufacturing and quality control. In our country, development of AVI technology is still in its infancy. The AVI systems applied by domestic enterprises are still relying heavily upon import products. It is strategically important to develop the AVI systems with self-owned intellectual property rights to meet the needs of domestic market.PC-based, software-dominated AVI systems, with the advanced features of economy and flexibility, have been gradually accepted by domestic enterprises, for the revamp and complement of facilities. An AVI system should be robust, fast and reliable. Many applications demand inspection tasks operate in real time. The performance of a PC-based AVI system relies mostly on its computational model. In this thesis, for the defect detection of tablets and blisters in pharmaceutical packaging, we introduced the target-tracking mechanism into AVI and proposed a novel inspection model, during which a "motion detection-target tracking-defect detection" strategy is adopted for moving target inspection. Based on this model, we pursued an in-depth research on the algorithms of motion detection, multi-object tracking and texture defect detection, respectively.Motion detection is the basis of object tracking. Based on the idea only to extract the silhouette of a moving object, we created a new three-frame differencing model, say, the MLR model, based on the maximum likelihood ratio of CV (Coefficient of Variation) of successive blocked frame regions. Simultaneously, a kernel-based density estimation model is used as a filter to suppress false positives of the motion areas detected by MLR model. Integrating it with the MLR model, we constructed the hybrid MLR-KDE model, which is robust and self-adaptive. To fulfill the need of multi-object tracking algorithms in chapter three, we proposed a fast boundary tracing algorithm, namely the RW (Roller Wheel) method. This algorithm performs well even the processed regions are non-connected.