Abstract
Prior work has shown Convolutional Neural Networks (CNNs) trained on surrogate Computer Aided Design (CAD) models are able to detect and classify real-world artefacts from photographs. The applications of which support twinning of digital and physical assets in design, including rapid extraction of part geometry from model repositories, information search & retrieval and identifying components in the field for maintenance, repair, and recording. The performance of CNNs in classification tasks have been shown dependent on training data set size and number of classes. Where prior works have used relatively small surrogate model data sets (< 100 models), the question remains as to the ability of a CNN to differentiate between models in increasingly large model repositories.This paper presents a method for generating synthetic image data sets from online CAD model repositories, and further investigates the capacity of an off-the-shelf CNN architecture trained on synthetic data to classify models as class size increases. 1,000 CAD models were curated and processed to generate large scale surrogate data sets, featuring model coverage at steps of 10◦, 30◦, 60◦, and 120◦ degrees.The findings demonstrate the capability of computer vision algorithms to classify artefacts in model repositories of up to 200, beyond this point the CNN’s performance is observed to deteriorate significantly, limiting its present ability for automated twinning of physical to digital artefacts. Although, a match is more often found in the top-5 results showing potential for information search and retrieval on large repositories of surrogate models.
Highlights
Recent work has shown the viability of synthetic image dataset generation, whereby a surrogate Computer Aided Design (CAD) model is processed and rendered with computer graphics software to generate two-dimensional artefact representations [13, 4]
This section reports the results from our study into a CNN trained on a CAD surrogate model dataset
At 1,000 classes, the CNN was still able to classify the matching model in the Top-5 predictions 75% of the time. This is a promising indication that CNNs could support design information search & retrieval applications
Summary
Recent trends in digital design, such as Twinning [6] have tahnedsebamrceotdheosd)sotrydpiirceacltlyscraenqnuinirge (me.go.dpifihcoattoigornamtomtehteryp),hwyshiecrael undReerlciennetdttrheendvsaliunedoigf irtapl ide, soigr nfu, lsluycihntaesgrTawteidn,nsiynngc[h6r]onhiasvae1tu.inoIdnnetrbrleointdweudecettnhioenpvhaylsuiecaolfarnadpid,igoirtafludlloyminatiengsratoteda,ccseylnecrhatreonpisroactieosnsebseatwndeeennhpahnycseicaanlalayntdic dciagpiatablilditoym. We consider an artefact to be a designed object, whose form can be distinguished and classified. The paper proceeds to present related works in the field of CNN use in design (Section 2), followed by a methodology for testing CNN scalability (Section 3). Results are reported (Section 5) and a discussion ensues with respect to CNN’s and their ability to support twinning activities in design (Section 6). To-date the effect of model repository size on the efficacy of this approach has not been investigated. Where CNN detection accuracy will typically decrease as repository size increases, the extensive training sets producible via surrogate models give scope to substantially increase performance
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