What does this research mean for the field?

An optimized lightweight YOLOv11 model integrating C3k2MBNV2, SCDown, and C3k2WTDC reduces parameter count by 47.4% and computational complexity by 38.0% while simultaneously improving detection precision and recall for high-voltage line insulators. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Develop an optimized, lightweight YOLOv11n deep learning model to enable efficient, real-time detection of high-voltage transmission-line insulators on resource-constrained aerial and edge devices.

August 23, 2026EnergiesOpen Access

An Enhanced Lightweight YOLOv11 Algorithm for Real-Time Detection of High-Voltage Line Insulators

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Authors

AKAbdil KarakanAfyon Kocatepe University

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Overview

Algorithm evaluation demonstrates enhanced detection accuracy and reduced computational load in aerial power line imagery, indicating viable edge deployment for unmanned aerial vehicles.

Key Points

Develop an optimized, lightweight YOLOv11n deep learning model to enable efficient, real-time detection of high-voltage transmission-line insulators on resource-constrained aerial and edge devices.
Integrated C3k2MBNV2 modules to reduce model complexity, SCDown layers to preserve spatial features during down-sampling, and C3k2WTDC modules to improve multi-frequency feature representation.
Trained and evaluated the modified YOLOv11n architecture using a dataset of 5,750 aerial insulator images captured across various angles, environmental conditions, and background settings.
Reduced total parameters from 6.20 M to 3.26 M (a 47.4% reduction) and computational complexity from 20.5 to 12.7 GFLOPs (a 38.0% reduction).
Increased detection precision from 91.3% to 93.8%, recall from 73.4% to 75.2%, mAP50 from 71.2% to 73.9%, and mAP50–95 from 65.6% to 67.3%.

Cite This Study

Abdil Karakan (2026) studied this question.

synapsesocial.com/papers/6a8aadf37677a341144468fa https://doi.org/10.3390/en19163939

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August 23, 2026EnergiesOpen Access

An Enhanced Lightweight YOLOv11 Algorithm for Real-Time Detection of High-Voltage Line Insulators

View Full Paper

Ask AI

Bookmark

Authors

AKAbdil KarakanAfyon Kocatepe University

Discussion

Member takes

Overview

Algorithm evaluation demonstrates enhanced detection accuracy and reduced computational load in aerial power line imagery, indicating viable edge deployment for unmanned aerial vehicles.

Key Points

Develop an optimized, lightweight YOLOv11n deep learning model to enable efficient, real-time detection of high-voltage transmission-line insulators on resource-constrained aerial and edge devices.
Integrated C3k2MBNV2 modules to reduce model complexity, SCDown layers to preserve spatial features during down-sampling, and C3k2WTDC modules to improve multi-frequency feature representation.
Trained and evaluated the modified YOLOv11n architecture using a dataset of 5,750 aerial insulator images captured across various angles, environmental conditions, and background settings.
Reduced total parameters from 6.20 M to 3.26 M (a 47.4% reduction) and computational complexity from 20.5 to 12.7 GFLOPs (a 38.0% reduction).
Increased detection precision from 91.3% to 93.8%, recall from 73.4% to 75.2%, mAP50 from 71.2% to 73.9%, and mAP50–95 from 65.6% to 67.3%.

Cite This Study

Abdil Karakan (2026) studied this question.

synapsesocial.com/papers/6a8aadf37677a341144468fa https://doi.org/10.3390/en19163939

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