Abstract
We are now developing the multi-purpose pork deboning robot system which consists of multi robot arms. In this system, in order to slit optimally and archive higher yield, it is required to detect the pubic bone and the tailbone exposed on the surface of the pork ham. However, it is difficult to recognize these bone surfaces by the image processing because the form of a ham changes with progression of the cut process and also some of pieces of the meat are covered over exposed bones. In this study, we developed the new technique to detect exposed bone regions and feature points by combining 3D image processing and deep learning (semantic pixel-wise segmentation). As a result, a fat area, a pubic bone area, and a tail bone area can be inferred by the originally pre-trained model using SegNet, and feature points can be detected independent of surrounding foreign objects.
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