What question did this study set out to answer?

The aim is to develop an efficient and accurate model for detecting insulator defects using unmanned aerial vehicles.

A Lightweight and Efficient Insulator Defect Detection Model for Unmanned Aerial Vehicle Inspection

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Demonstrates a lightweight model achieving high accuracy in insulator defect detection using UAVs, suggesting improved power grid safety.

The aim is to develop an efficient and accurate model for detecting insulator defects using unmanned aerial vehicles.
Proposed a lightweight model based on YOLOv11 with Faster-Net as the backbone.
Utilized partial convolution to optimize computational costs.
Constructed neck network using hierarchical feature fusion blocks for multiscale feature fusion.
Integrated a Parallel Patch-aware Attention module to focus on key defect areas.
Included a Lightweight Adaptive Extraction module to enhance feature calculation.
Achieved a mAP50 accuracy rate of 93.2% and precision of 93.7% on public datasets.
Model has a parameter count of only 2.3 M and computational load of 4.6 GFLOPs.
Confirmed effectiveness through ablation experiments and cross-dataset tests.
Successfully deployed on Jetson Xavier NX platform and M350 UAV for real-time detection.

Fang et al. (2026) studied this question.

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View Full Paper

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Authors

Demonstrates a lightweight model achieving high accuracy in insulator defect detection using UAVs, suggesting improved power grid safety.

The aim is to develop an efficient and accurate model for detecting insulator defects using unmanned aerial vehicles.
Proposed a lightweight model based on YOLOv11 with Faster-Net as the backbone.
Utilized partial convolution to optimize computational costs.
Constructed neck network using hierarchical feature fusion blocks for multiscale feature fusion.
Integrated a Parallel Patch-aware Attention module to focus on key defect areas.
Included a Lightweight Adaptive Extraction module to enhance feature calculation.
Achieved a mAP50 accuracy rate of 93.2% and precision of 93.7% on public datasets.
Model has a parameter count of only 2.3 M and computational load of 4.6 GFLOPs.
Confirmed effectiveness through ablation experiments and cross-dataset tests.
Successfully deployed on Jetson Xavier NX platform and M350 UAV for real-time detection.

Fang et al. (2026) studied this question.

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