Unique Data Balancing Method for Defect Detection in Power Transmission and Transformation

This paper presents a unique data balancing approach using power transmission and transformation defect detection as an example. The proposed method achieves a more precise balance compared to traditional oversampling methods. It takes into account specific dataset characteristics and constraints to ensure effective sample adjustment. The experimental results demonstrate the superiority of this approach in enhancing object detection and classification accuracy within the defect detection context, providing a promising solution for improving data quality and model performance in relevant fields.