Aiming at automating the defect identification process in Akita Shizuki, a local company suffering the effects of the aging Japanese population, we present the base for a cost-efficient solution for the identification of defects on the surface of film-capacitors based on image processing and machine learning with a simple web camera. As a result of this study, a model with classification accuracy of over 90 % was achieved in the offline classification of optimal and defective film-capacitors in a 5-fold cross validation test. Testing for online classification was also performed with a limited number of physical samples, but results suggest that adjustments to our methodology need to be done to report further. Ultimately, we found that it is possible to identify defects on the surface of film capacitors with a simple offline classification system based on machine learning for the recognition of a single type of capacitor among the many produced at Akita Shizuki. Furthermore, the possibility of doing this in real time was explored, and could be pursued if there is the need for such a system in the current production line environment.

In this paper, we propose a new three-dimensional (3-D) measurement method of objects in unknown liquid with a stereo vision system. When applying vision sensors to measuring objects in liquid, light refraction is an important problem. Therefore, we estimate refractive indices of unknown liquids by using images of water surface, restore images that are free from refractive effects of the light, and measure 3-D shapes of objects in liquids in consideration of refractive effects. The effectiveness of the proposed method is shown through experiments.