Enhancing automated defect detection in collagen based manufacturing by employing a smart machine vision technique

Machine vision is now being extensively used for defect detection in the manufacturing process of collagen-based products such as sausage skins which is a multimillion dollar industry worldwide. At present the industry standard is to use a LabView software environment, whereby a graphical interface is used to manage and detect any defects in the collagen skins. Available data corroborates that this method allows for false positives to appear in the results where creases or folds are resolved to be defects in the product instead of being a by-product in the inspection process. This is directly responsible for reducing the overall system performance and resulting wastage of resources. Hence novel criteria were added to enhance the current techniques used with defect detection as elaborated in this paper. The proposed improvements aim to achieve a higher accuracy in detecting both true and false positives by utilizing a function that probes for the color deviation and fluctuation in the collagen skins. From the operating point of view, this method has a more flexible approach with a higher accuracy than the original graphical LabView program and could be incorporated into any programming environment. After implementation of the method in a well-known Australian company, investigational results demonstrate an average 26% increase in the ability to detect false positives with a corresponding substantial reduction in operating cost.