Detection of Door-Closing Defects by Learning from Physics-Based Simulations

In this paper, we propose a method that applies physics-based simulations for detecting door-closing defects. Quantitative inspection of industrial products is essential to reduce human errors and variation in inspection results. Door-closing inspections, which now rely on human sensory evaluation, are prime targets for quantification and automation. Developing a visual inspection model based on deep learning requires time-consuming and labor-intensive data collection with dedicated measuring instruments. To eliminate the need for expensive data collection, our proposal uses physics-based simulation data instead of real data to learn the physical relationships. Specifically, we simultaneously learn a binary classification task for normal and defective doors and a task for estimating door-closing energy while sharing parameters, which allows us to learn the relationships between them in a preliminary step. Experiments demonstrate that our method has greater accuracy than existing methods and achieves an accuracy comparable to the method that uses ground-truth data collected with dedicated measuring instruments.