Concept Towards Segmenting Arm Areas for Robot-Based Dermatological In Vivo Measurements

Authors

DOI:

https://doi.org/10.52825/thwildauensp.v1i.34

Keywords:

Object Detection, Convolutional Neural Networks, RGB-D images

Abstract

Dermatological in vivo measurements are used for various purposes, e.g. health care, development and testing of skin care products or claim support in marketing.  Especially for the last two purposes, in vivo measurements are extensive due to the quantity and repeatability of the measurement series. Furthermore, they are   performed manually and therefore represent a nonnegligible time and cost factor. A solution to this is the implementation of collaborative robotics for the  measurement execution. Due to various body shapes and surface conditions, common static control procedures are not applicable. To solve this problem, spatial  information obtained from a stereoscopic camera can be integrated into the robot control process. However, the designated measurement area has to be detected  and the spatial information processed. Therefore the authors propose a concept towards segmenting arm areas through a CNN-based object detector and their  further processing to perform robot-based in vivo measurements. The paper gives an overview of the utilization of RGB-D images in 2D object detectors and  describes the selection of a suitable model for the application. Furthermore the creation, annotation and augmentation of a custom dataset is presented.

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Published

2021-06-15