An application of Generative Adversarial Networks to improve automatic inspection in automotive manufacturing

In manufacturing systems, the quality of inspection is a critical issue. This can be conducted by humans or by employing Computer Vision Systems (CV S), which are trained upon representative datasets of images to detect classes of defects that may occur. The construction of such datasets strongly limits the use of CV S methods, as the variety of defects has combinatorial nature. Alternatively, instead of recognizing defects, a system can be trained to detect non-defective standards, becoming appropriate for some application profiles. In automotive manufacturing, for example, parts are assembled within a reduced set of correct combinations, while the amount of possible incorrect assembling is enormous. This paper integrates a Generative Adversarial Network (GAN) within the CV S framework used by Renault/Brazil to improve the detection of defective production in its automotive assembly line. By sparing the construction of expensive defect image datasets, our solution has proved to be cost-effective and more efficient in comparison with the current CV S solution to detect defects, besides generalizing better to inspect different components without any modification in the method.