A Generative Adversarial Network approach for automatic inspection in automotive assembly lines

In manufacturing systems, quality of inspection is a critical issue. This can be conducted by humans, or by employing Computer Vision Systems (CVS) 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 CVS 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 flexible automotive manufacturing, for example, parts are assembled within a reduced set of correct combinations, while the amount of possible incorrect assembling is enormous. In this paper, we show how a CVS can be extended with a Deep Learning-based approach that exploits a Generative Adversarial Network (GAN) to detect non-defective production, eliminating the need for constructing defect image datasets. The proposal is tested over the assembly line of Renault, in Brazil. Results show that our method returns better accuracy in inspection, compared with the current CVS solution, besides generalizing better to different components inspection without having to modify the method.