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This is a Experimental Study study (also classified as: Quantitative Study).

DM-YOLO: Transmission Line Fault Detection Based on Dynamic Multi-scale Convolution and Attention Mechanism

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Proposed system shows improved detection accuracy for multi-scale faults, highlighting enhanced feature extraction and attention mechanisms.

The DM-YOLO algorithm achieves an average accuracy of 93.8% in detecting transmission line faults.
A dynamic multi-scale convolution module improves the model's feature extraction capabilities for varying target sizes.
The multi-dimensional perceptual attention module enhances detection accuracy through improved regional correlations.
The multi-head feature fusion module strengthens the network's understanding of both semantic and textural information.

Hao et al. (2025) studied this question.

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View Full Paper

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Authors

Proposed system shows improved detection accuracy for multi-scale faults, highlighting enhanced feature extraction and attention mechanisms.

The DM-YOLO algorithm achieves an average accuracy of 93.8% in detecting transmission line faults.
A dynamic multi-scale convolution module improves the model's feature extraction capabilities for varying target sizes.
The multi-dimensional perceptual attention module enhances detection accuracy through improved regional correlations.
The multi-head feature fusion module strengthens the network's understanding of both semantic and textural information.

Hao et al. (2025) studied this question.

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