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This is a Experimental Study study.

GLE-YOLO:A Lightweight and Efficient Defect Detection Model for Power Inspection

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This observational analysis finds 88.7% precision in power inspection defects, suggesting GLE-YOLO optimizes detection speed and accuracy.

The GLE-YOLO model achieves 88.7% precision and 77.5% recall, showcasing its effectiveness in defect detection.
Using a lightweight design, GLE-YOLO processes at 90.4 frames per second, improving operational efficiency in inspections.
The model incorporates a Generalized Efficient Layer Aggregation Network, addressing sample imbalance and variance issues.
Optimizing the bounding box loss function enhances convergence speed for real-time applications in power inspection.

Zhou et al. (2025) studied this question.

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View Full Paper

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Authors

This observational analysis finds 88.7% precision in power inspection defects, suggesting GLE-YOLO optimizes detection speed and accuracy.

The GLE-YOLO model achieves 88.7% precision and 77.5% recall, showcasing its effectiveness in defect detection.
Using a lightweight design, GLE-YOLO processes at 90.4 frames per second, improving operational efficiency in inspections.
The model incorporates a Generalized Efficient Layer Aggregation Network, addressing sample imbalance and variance issues.
Optimizing the bounding box loss function enhances convergence speed for real-time applications in power inspection.

Zhou et al. (2025) studied this question.

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