Painted Surface Inspection using Deep learning and Morphological Segmentation
This projectdescribes the design and implementation of a novel inspection system for detecting defects on car bodies based on artificial vision,. The system is based on the principle of performing a lightning sweeping with static imagining system, which causes shadows surrounding defects when merging consecutive images, coined as defect augmentation phenomena. As a result, we can detect millimetric defects of 0.3mm diameter or greater with different shapes which were very hard to detect with existing technology without that phenomena. The main innovation of this industrial project is the development of a system that improves in almost 100% the human inspection. As a consequences, it reduces the number of invalid vehicles, energy consumption, saving painting which also implies a significant cost reduction. It also improves working conditions for workers by reducing ocular fatigues.