X-Ray-Based Lightweight High-Performance Non-Destructive Defect Detection Method for High-Voltage Cables

Abstract

High-voltage cable defect detection is an important part of urban power system maintenance. Due to variations in cable thickness, large weight, and challenging operability, existing signal-processing and artificial-intelligence-based methods may face trade-offs among detection accuracy, cost, efficiency, and applicability. Moreover, some existing methods are invasive in nature. Therefore, this paper proposes an improved multiscale attention-based YOLOv8 model, designs a portable X-ray non-destructive testing (NDT) system, and creates a larger dataset based on the CycleGAN-based (Cycle Consistent Adversarial Networks-based) Pix2Pix (Pixel-to-Pixel Translation) model. First, the improved YOLOv8 model introduces EfficientNet to replace CSPNet (Cross Stage Partial Network) as the backbone network and incorporates a Squeeze-and-Excitation (SE) module after each convolutional block. Second, a bottom-up feature pyramid PAN (Path Aggregation Network) is integrated with the FPN (Feature Pyramid Network) to construct a hybrid multi-scale feature fusion structure, enabling multi-level and multi-directional information transfer. Moreover, a task-specific box refinement strategy combining K-means initialization with IoU-guided matching is employed to improve localization accuracy. The experimental results show that, compared with the other detection models, EMA-YOLOv8 improves the average precision and mAP50 by 4.68 and 3.9 percentage points, respectively, while reducing the average number of parameters and GFLOPs by 35.37% and 27.76%, respectively. Therefore, the proposed model has great potential.