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February 16, 2026Scientific DataOpen Access

A Defect Dataset for Electrode Coating Manufacturing

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Authors

VSVignesh SampathALAndrew S. LeeSMSamuel David Miller

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Overview

Dataset enables machine learning for automated defect detection in electrode manufacturing, indicating advancements in quality control.

Key Points

  • To present a dataset that supports automated defect detection for electrode coating processes.
  • Developed the CoatingVision dataset with labeled defect types
  • Included high-resolution images for defect segmentation and classification
  • Provided an open-source codebase for AI model evaluation
  • CoatingVision contains over 2,200 image samples
  • Supports multi-label classification for various defect types
  • Facilitates benchmarking and reproducibility in defect detection research

Cite This Study

Sampath et al. (2026) studied this question.

synapsesocial.com/papers/69926a620d0ce0adc9976a38https://doi.org/10.1038/s41597-025-06419-1
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Non-Invasive Coating Surface Defect Detection Through Visual Assessment and Multimodal Validation2026
  2. 2In-line quality control for electrode manufacturing: Advanced sensing and defect detection in battery production2026
  3. 3A PRISMA Driven Systematic Review of Publicly Available Datasets for Benchmark and Model Developments for Industrial Defect Detection2024
  4. 4Development of Data Preprocessing Algorithm for Coating Process AI Model2026
  5. 5Defects Detection of Lithium-Ion Battery Electrode Coatings Based on Background Reconstruction and Improved Canny Algorithm2024 · 6 citations