Work

Defect Detection in GIS X-Ray Images Based on Improved YOLOv10

Year: 2025

Type: article

Abstract: Timely and accurate detection of internal defects in Gas-Insulated Switchgear (GIS) with X-ray imaging is critical for power system reliability. However, automated detection faces significant challenges from small, low-contrast defects and complex background structures. This paper proposes an enhanced object-detection model based on the lightweight YOLOv10n framework, specifically optimized for this task. Key improvements include adopting the Normalized Wasserstein Distance (NWD) loss function for small object localization, integrating Monte Carlo (MCAttn) and Parallelized Patch-Aware (PPA) attention to enhance feature extraction, and designing a GFPN-inspired neck for improved multi-scale feature fusion. The model was rigorously evaluated on a custom GIS X-ray dataset. The final model achieved a mean Average Precision (mAP) of 0.674 (IoU 0.5:0.95), representing a 5.0 percentage point improvement over the YOLOv10n baseline and surpassing other comparative models. Qualitative results also confirmed the model's enhanced capability in detecting challenging small and low-contrast defects. This study presents an effective approach for automated GIS defect detection, with significant potential to enhance power grid maintenance efficiency and safety.

Source: Sensors

DOI: 10.3390/s25175310

Authors: Guoliang Xu, Xiaolong Bai, Menghao Huang

Institution: Chongqing University of Posts and Telecommunications

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137