深層学習を用いた軟弱野菜の認識と野菜調製システムへの応用

抄録

This paper describes an automatic post harvesting system of tender leafy vegetables. There is a problem that is difficult to recognize vegetables in crowd environment. The system takes RGBD images, recognizes vegetables using an instance segmentation method, calculate pose of vegetable, picks and places it at a conveyer. We chose Komatsuna as example of tender leafy vegetables. We make an annotation tool and generate datasets training the Mask R-CNN network. We show that the network recognizes komatsuna with high accuracy when we use 2-class dataset, including the whole-komatsuna class and the part-komatsuna class. We also can recognised a successful manipulation of the vegetable, grasp with good accuracy when there are few komatsuna.