Quality assessment methodology based on machine learning with small datasets: Industrial castings defects

Nowadays there are numerous problems for which use of a multi-objective in image classification would be desirable although, unfortunately, the number of samples is too low. In these situations, higher level classifications could also work (for example, in surface defect detection, it is important to identify the defect, but it could also be useful to detect whether or not the object has a defect). To this end, we present a methodology called BoDoC which allows to improve this classification. To evaluate the methodology, we have created a new dataset, obtained from a foundry, to detect surface errors in casting pieces with 2 different defects: (i) inclusions, (ii) coldlaps and (iii) misruns. We also present a collection of techniques to select featu res from the images. We prove that our methodology improves the direct classification results in real world scenarios, with 91.305% precision.