We propose the use of a deep network to detect, segment and characterize a Coordinate Measuring Machine (CMM) probe used in measuring various machine parts. Our motivation is to accelerate the time taken for an operator to input various parameters of a CMM probe into the system, so that delay in quality assurance of machine parts can be negated. Using imagery from a high resolution EO sensor, we design a probe recognition and characterization framework which can segment probe regions, classify various probe-region proposals into generic or specific probe components, and estimate the various configuration parameters of the probe. In order to measure a specific machine part, an operator provides the CMM machine with an image of an assembled probe. This end-to-end deep network-based framework will then generate configuration parameters suitable for the measurement task. Since the number of machine parts are in the order of thousands, the probe can have multiple configurations. In this work, we do extensive analysis on a probe dataset captured in our lab and evaluate two main aspects of the framework: its ability to segment regions, and classify those regions as probe components.
Binu M. Nair, Vidur Prasad, Nilesh Powar, "Detection and characterization of Coordinate Measuring Machine (CMM) probes using deep networks for improved quality assurance of machine parts." in Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World, 2017, pp 37 - 44, https://doi.org/10.2352/ISSN.2470-1173.2017.10.IMAWM-164