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IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...

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

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Published on: December 15, 2023

Infrared image target detection for substation electrical equipment based on improved faster region-based

Changdong Wu¹, Yanliang Wu¹, Xu He¹

¹School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China.

The Review of Scientific Instruments

|April 10, 2024

Summary

This study introduces an improved Faster R-CNN algorithm for detecting electrical equipment in low-quality infrared images. The enhanced model achieves superior accuracy and recall, improving substation safety and maintenance.

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Area of Science:

Electrical Engineering
Computer Vision
Artificial Intelligence

Background:

Substation electrical equipment generates numerous low-quality infrared images.
Traditional target detection algorithms struggle with feature extraction from these images.

Purpose of the Study:

To propose an improved Faster R-CNN algorithm for accurate identification of electrical equipment in infrared images.
To enhance feature extraction capabilities for better detection performance.

Main Methods:

Modified Faster R-CNN with an InResNet backbone for richer feature extraction.
Replaced Rectified Linear Unit (ReLU) with Exponential Linear Unit (ELU) activation.
Incorporated group normalization and dense connections into the ResNet-34 network, creating a residual dense connection network.

Main Results:

The improved Faster R-CNN demonstrated the highest mean average precision and average recall for most substation electrical equipment.
Outperformed original Faster R-CNN, Single-Shot MultiBox Detector (SSD), and YOLOv3-SPP in detection confidence and prediction box accuracy.

Conclusions:

The proposed enhanced Faster R-CNN algorithm effectively addresses the challenges of detecting electrical equipment in low-quality infrared images.
The modifications significantly improve detection accuracy, confidence, and localization, offering a robust solution for substation monitoring.