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A Standardized PMML Format for Representing Convolutional Neural Networks with Application to Defect Detection.

Max Ferguson1, Yung-Tsun Tina Lee2, Anantha Narayanan3

  • 1Civil and Environmental Engineering, Stanford University, Y2E2 Building, 473 Via Ortega, Stanford, CA 94305, USA.

Smart and Sustainable Manufacturing Systems
|October 8, 2020
PubMed
Summary

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This summary is machine-generated.

This study proposes a standardized format for convolutional neural networks (CNNs) using Predictive Model Markup Language (PMML) to improve model management and interoperability in engineering and manufacturing. A novel PMML schema and scoring engine were developed and benchmarked for practical applications like defect detection.

Area of Science:

  • Computer Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Convolutional Neural Networks (CNNs) are widely used in engineering and manufacturing for image processing.
  • Challenges exist in managing and distributing trained CNN models due to a lack of standardized representation and poor framework interoperability.

Purpose of the Study:

  • To propose a standardized format for CNNs based on Predictive Model Markup Language (PMML).
  • To develop a new schema for representing various CNN systems (classification, regression, semantic segmentation).
  • To demonstrate practical application and evaluate performance of the proposed standard.

Main Methods:

  • A novel standardized schema for CNN representation using PMML was developed.
  • A high-performance scoring engine was created to evaluate images and videos against PMML models.
Keywords:
Automated Surface InspectionConvolutional Neural NetworksDefect DetectionImage ProcessingMachine Learning ModelsPredictive Model Markup LanguageSmart ManufacturingStandard

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  • Benchmarking was conducted on different computational platforms to assess performance.

    • Main Results:

    • The proposed PMML format successfully represented a semantic segmentation model for detecting casting defects in X-ray images.
    • The developed scoring engine demonstrated effective evaluation of images and videos.
    • Performance benchmarking provided insights into the utility across various platforms.

    Conclusions:

    • The proposed PMML-based standard addresses the need for standardized CNN representation and improves interoperability.
    • The developed schema and scoring engine offer a practical solution for managing and deploying CNN models in industrial applications.
    • The study validates the effectiveness and utility of the proposed standard through performance evaluation.