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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Comparison of CNN-Based Image Classification Approaches for Implementation of Low-Cost Multispectral Arcing

Elizabeth Piersall¹, Peter Fuhr¹

¹Oak Ridge National Laboratory, Electrification and Energy Infrastructures Division, Oak Ridge, TN 37830, USA.

Sensors (Basel, Switzerland)

|February 27, 2026

Summary

Affordable camera sensing using machine learning and ensemble models can successfully detect electrical arcing. This approach offers a cost-effective alternative to expensive sensors for various applications.

Keywords:

convolutional neural networks machine learning multispectral sensing

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Area of Science:

Electrical engineering
Computer vision
Machine learning

Background:

Camera-based sensing advancements are driven by machine learning and Unmanned Aerial Vehicle (UAV) technology.
High costs of advanced sensors limit adoption, necessitating exploration of affordable alternatives.

Purpose of the Study:

To evaluate the efficacy of low-cost camera sensing systems for detecting electrical arcing.
To investigate the use of machine learning-based image classification and ensemble models for sensor fusion with accessible hardware.

Main Methods:

Developed custom datasets for training and validation of deep learning image classification models.
Utilized ensemble models for sensor fusion, applying identical models across different cameras to minimize technical overhead.
Evaluated performance using validation datasets with varying difficulty levels to assess custom data robustness.

Main Results:

Camera-based detection using machine learning proved successful for identifying electrical arcing with representative training data.
Ensemble models incorporating diverse data sources effectively mitigated risks associated with training data gaps.
Data fusion models demonstrated capability without camera-specific design, enabling the use of less specialized equipment.

Conclusions:

Machine learning with affordable, accessible camera hardware offers a viable alternative for electrical arcing detection.
Ensemble modeling enhances system reliability by integrating multiple data sources, though redundancy may decrease without additional precautions.
This approach democratizes advanced sensing capabilities by reducing reliance on expensive, specialized equipment.