In-line inspection solution for codes on complex backgrounds for the plastic container industry

Abstract

Machine vision technologies have been widely used for automating the product quality control, but the defect inspection for codes on complex backgrounds is still a challenging task in the plastic container industry. In this work, an efficient and accurate inspection solution based on deep learning was proposed aiming at the detection of codes on complex backgrounds for the plastic container such as beverage packages. Firstly, image processing algorithms such as the region translation method, morphological processing, and image matching technology based on SIFT (Scale Invariant Feature Transform) features were implemented to generate synthetic defective samples, which moderated the class-imbalance problem. Data augmentation strategies were used to increase the amount of training data. Secondly, the ShuffleNet V2 framework was adapted to inspect inkjet codes on complex backgrounds. Additionally, the transfer learning was used to transfer the trained model to other inspection tasks for different kinds of packages. Finally, the proposed approach was built onto an in-line code inspection apparatus for the plastic container industry, and an accuracy of 0.9988 was achieved. The in-line testing results of false detection and omission detection rates demonstrated that the proposed solution can fully meet the production requirements. To the best of our knowledge, this report describes the first time that deep learning has been applied to the industrial defect inspection for the plastic container industry.