Feature extraction algorithm for fill level and cap inspection in bottling machine

Automated Visual Inspection Systems (AVIS) have a strong ability to improve bottle manufacturing quality control by means of inspecting products automatically instead of through manual inspections. AVIS automatically tends to make a suitable decision in process and results classification according to the images of the products via image processing and Artificial Intelligence techniques. Since bottling is one of the most common packaging styles in the food and medical industries, in this paper we will concentrate on the visual inspection of bottles. Checking the quality of the cap closure and over-filling/under-filling checks for the level of the liquid in the bottle have been investigated to reach an optimized bottle product. Therefore, in this research general hardware and modules for these systems are investigated. Besides, new techniques of bottle inspection are reviewed along with presenting previous work of other researchers. Subsequently we will propose a feature extraction algorithm to inspect cap closure and level of the liquid in the bottle together, in the same system. According to the new proposed method, our system classifies three situations for cap condition and three situations for the condition of the level of the liquid. As a result the system has investigated 9 situations. The algorithm of the system will accept its system when the liquid level is in the correct position and the cap is in the normal condition. Other situations will be rejected. The proper algorithm which is proposed here using bottle visual inspection techniques leads our system to reach an optimized liquid level with a high quality of the cap closure.