Aspects of image data preparation to extend a classification scheme for cleaning mechanisms to realistic soils

Abstract

AbstractKnowledge of the cleaning mechanism is necessary to choose a suitable model for a cleaning simulation. In the present work, an existing classification scheme for cleaning mechanisms is considered. Altough this framework is quite promising, the generation of training data constitutes a bottleneck, since the labeling was done manually and very roughly in order to supply the necessary amount of samples in a reasonable time. This, in turn, causes the scheme to be inaccurate when applied to more realistic data. The aim of the present work is to improve the preparation of training data preparation by introducing a semi‐automatic labeling procedure. The labeling procedure involves a new perspective on the data and the application of a gradient filter procedure. Furthermore, fully convolutional networks (FCNs) are employed to generalize different gradient filter. The labeling procedure is significantly faster and more consistent than manual labeling. Also, a proof of concept is provided showing that the FCNs are a suitable technique for the present classification task.