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GURLKNet gated unified reparameterized large kernel network for insulator defect detection.

Xun Li^1,2,3, Yuzhen Zhao⁴, Yang Zhao¹

¹Xi'an Key Laboratory of Advanced Photo-electronics Materials and Energy Conversion Device, School of Electronic Information, Xijing University, Xi'an, 710123, People's Republic of China.

Scientific Reports

|November 6, 2025

Summary

A new Gated Unified Reparameterized Large Kernel Network (GURLKNet) improves unmanned aerial vehicle (UAV) based insulator defect detection. This method enhances accuracy and efficiency for power system safety inspections.

Keywords:

Defect detection Feature fusion network Insulator Sobel operator UAV aerial image YOLO

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Area of Science:

Computer Vision
Artificial Intelligence
Electrical Engineering

Background:

Unmanned aerial vehicles (UAVs) and computer vision are vital for power system safety.
Insulator defect detection faces challenges like scale imbalance, blurred edges, and complex backgrounds.
Existing methods struggle with the nuances of aerial inspection data.

Purpose of the Study:

To develop an advanced network for enhanced insulator defect detection using UAVs.
To address the limitations of current object detection models in complex power system environments.
To improve the accuracy and efficiency of intelligent insulator inspection.

Main Methods:

Proposes Gated Unified Reparameterized Large Kernel Network (GURLKNet) featuring a Gated Unified Reparameterized Large Kernel Module (GUR-LKM) for expanded receptive fields.
Introduces an Edge-Guided Feature Stem (EGFStem) integrating edge detection and texture guidance to enhance boundary perception.
Employs a Context-Interactive Fusion Network (CIFNet) with multi-scale attention for improved feature fusion and localization accuracy.

Main Results:

GURLKNet demonstrates strong overall accuracy and low computational cost on insulator defect datasets.
Outperforms mainstream object detection models on key evaluation metrics.
Achieved a 3.5% mAP50 improvement on Insulator-DET and 0.9% on IDID compared to baseline models.

Conclusions:

GURLKNet offers an efficient and reliable solution for intelligent insulator inspection.
The proposed method advances object detection technology for low-altitude power system sensing.
Facilitates the engineering application and deployment of advanced AI in power infrastructure maintenance.