What does this research mean for the field?

YOLOv10-SPD achieves 94.11% mAP@0.5 for substation defect detection while reducing model parameters by 76.09% and computational costs by 38.82%. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to develop a lightweight model for accurate defect detection in substations using multi-scale features.

February 28, 2026EnergiesOpen Access

Lightweight Defect Detection in Substations with Multi-Scale Features and Network Pruning

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Authors

TZTong ZhangChina Three Gorges University TWTian WuChina Three Gorges University ZOZhenhui OuyangChina Three Gorges University

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Implication

This research proposes YOLOv10-SPD to enhance defect detection accuracy in substations, implying efficient data management.

Key Points

The aim is to develop a lightweight model for accurate defect detection in substations using multi-scale features.
Developed YOLOv10-SPD model for defect detection
Introduced a Feature Modulation Module for enhanced defect detail perception
Designed a hybrid module for efficient feature map reconstruction
Created a Multi-Scale Context Modeling Module for compact multi-scale semantic representation
Implemented Weight-Magnitude-Based Hierarchical Pruning for model compression and efficiency
Achieved 94.11% mAP@0.5, outperforming baseline YOLOv10n by 5.14%
Reduced model parameters by 76.09%
Reduced computational costs by 38.82%
Demonstrated improved detection accuracy for small and multi-scale targets

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/69a2878e0a974eb0d3c034f5 https://doi.org/10.3390/en19051163

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Lightweight Defect Detection in Substations with Multi-Scale Features and Network Pruning

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Implication

Key Points

Cite This Study

Lightweight Defect Detection in Substations with Multi-Scale Features and Network Pruning

Discussion

Member takes

Implication

Key Points

Cite This Study

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