Medical Catheters Grasping Point Detection with Quality Control

Abstract

The ability to grasp objects is one of the basic functions of modern industrial robots. The emphasis of this paper is placed on the visual perception system, and in particular, on the data processing method leading to grasp point detection. The solution involved the design of a perceptual system in which it was necessary to use a SWIR sensor that can see through plastic bags and thus provide sufficient image information for possible processing by a neural network. The grasping point detection was tested with three convolutional neural network architectures. The method was evaluated by a generalized intersection over union (gIoU). The superior architecture was Attention U-Net, where gIoU reached 0.8522 in the case of the best model.