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Vision-Based Detection and Classification of Used Electronic Parts.

Praneel Chand¹, Sunil Lal²

¹Centre for Engineering and Industrial Design (CEID), Waikato Institute of Technology, Hamilton 3200, New Zealand.

Sensors (Basel, Switzerland)

|December 11, 2022

Summary

This summary is machine-generated.

This study developed vision-based methods to classify electronic components for reuse, addressing e-waste. Convolutional Neural Networks (CNNs) achieved the highest accuracy (98.1%) in identifying parts like capacitors and ICs.

Keywords:

convolutional neural networks (CNNs)deep learning object classification object detection shallow neural networks (SNNs)support vector machines (SVMs)vision system

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Area of Science:

Computer Vision
Machine Learning
Sustainable Electronics

Background:

Growing electronic waste (e-waste) necessitates sustainable solutions.
Reusing electronic parts is crucial for environmental and economic sustainability.
Automated classification of electronic components can facilitate reuse.

Purpose of the Study:

To develop and evaluate vision-based methods for detecting and classifying used electronic components.
To investigate the effectiveness of different machine learning models for this task.
To identify the most accurate classification method for common electronic parts.

Main Methods:

A customized object detection algorithm was used to identify regions of interest in an overhead camera view.
Three classification methods were compared: shallow neural networks (SNNs), support vector machines (SVMs), and deep learning convolutional neural networks (CNNs).
All models used 30x30 pixel grayscale image inputs.

Main Results:

Shallow neural networks (SNNs) achieved 85.6% accuracy.
Support vector machines (SVMs) with a cubic kernel and PCA (20 features) reached 95.2% accuracy.
Deep learning CNN models achieved the highest accuracy at 98.1%.

Conclusions:

Vision-based classification using machine learning is effective for identifying electronic components.
Deep learning CNNs offer superior performance for classifying electronic parts compared to SNNs and SVMs.
This technology can support the reuse of electronic components, contributing to sustainability.