A multilevel information fusion approach for visual quality inspection

For visual quality inspection systems to be applicable in industrial settings, it is mandatory that they are highly flexible, robust and accurate. In order to improve these characteristics a multilevel information fusion approach is presented. A first fusion step at the feature-level enables the system to learn from an undefined number of potential defects which might be segmented from the images. This allows for the quality control operators to label the data at the image-level and the sub-image-level, and use this information during the learning process. Additionally, the operators are allowed to provide a confidence measure for their labelling. The additional information obtained from the increased flexibility of the operator inputs allows to build more accurate classifiers. A second fusion step at the decision-level combines the classifications of different classifiers, making the system more accurate and more robust with respect to the classification method chosen. The experimental results, using various artificial and real-world visual quality inspection data sets, show that each of these fusion approaches can significantly improve the classification accuracy. If both information fusion approaches are combined the accuracy increases even further, significantly outperforming each of the fusion approaches on their own.

Research highlights
► A generic multilevel information fusion framework for visual inspection is presented.
► Trainable feature fusion aggregates the information about multiple potential defects.
► Trainable classifier fusion combines the decisions of different classifiers.
► Five artificial and nine industrial data sets are used for experimental validation.
► Both fusion approaches lead to a significantly increased classification performance.