A Novel Vision-based Approach for Detection of Foreign Substances

The detection of foreign substances in injection so far is still achieved artificially, which result in low accuracy and low efficiency. This paper focuses on developing a novel vision-based approach for detection of foreign substances. Foreign substances are classified into two categories, subsiding-slowly object and subsiding-fast object. A relative movement caused by a motor helps to distinguished foreign substances from ampoule surface scratches. Moving objects in injection are divided from static ones by a background image derived from two frames. The Mean Shift Embedded Particle Filter (MSEPF) is proposed to detect moving-slowly object while Frame Distance is defined to detect moving-fast object. 200 ampoule samples filled with injection are tested. The integrated detection accuracy with this approach is 98.00%, with 97.56% accuracy for subsiding-slowly objects and 96.67% accuracy for subsiding-fast ones. The result shows that the system can detect foreign substances effectively.