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YOLO-CSS: a lightweight defect detection model for complex substation scenarios

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YOLO-CSS improves defect detection accuracy and reduces computational costs in substation environments, highlighting its efficiency.

YOLO-CSS enhances defect detection accuracy while minimizing computational costs in substation scenarios.
Experimental results showed a 32.8% reduction in parameters and 27.2% in computational costs compared to YOLOv8.
The model employs advanced feature extraction and a lightweight detection head for real-time performance.
Cross-dataset evaluations demonstrate the model's robust generalization capability in diverse environments.

Ming et al. (2025) studied this question.

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