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JUHCCR-v1: a database for hand-drawn electrical and electronics circuit component recognition.

Ayush Roy1, Saptarshi Pani1, Samir Malakar2

  • 1Department of Electrical Engineering, Jadavpur University, Kolkata, 700032, India.

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This summary is machine-generated.

Researchers developed a new dataset for recognizing hand-drawn electrical components. A deep learning model achieved 91.15% accuracy, advancing automated circuit analysis.

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Machine Learning

Background:

  • Recognizing electrical and electronics components from hand-drawn circuits is a significant challenge.
  • A lack of publicly available datasets has hindered progress in this research area.

Purpose of the Study:

  • To address the lack of datasets for hand-drawn circuit component recognition.
  • To develop and release a comprehensive dataset for training deep learning models.
  • To establish a baseline performance for circuit component recognition.

Main Methods:

  • Created JUHCCR-v1, a dataset of 20 common hand-drawn circuit components.
  • Generated a synthetic dataset with variations (orientations, distortions) of components.
  • Developed a weighted ensemble method using a DenseNet-121 architecture with a convolutional block attention module.

Main Results:

  • The developed dataset (JUHCCR-v1) and synthetic variations are publicly available.
  • The benchmarking method achieved 91.15% accuracy on test set images.
  • The publicly available dataset and code aim to facilitate further research.

Conclusions:

  • The creation of the JUHCCR-v1 dataset is a crucial step towards advancing automated circuit analysis.
  • The proposed deep learning approach provides a strong baseline for component recognition.
  • Publicly releasing the dataset and code will foster community-driven advancements in the field.