Work

EMU fault detection algorithm based on the GAN+YOLO model under limited data

Year: 2025

Type: article

Abstract: Electric multiple units (EMUs) fault detection under the Train-side EMU Fault Detection System (TEDS) faces persistent challenges due to data scarcity and frequent missed detections of small or cross-scale fault targets. This paper proposes a dual-enhanced detection framework that integrates high-fidelity data generation with a structurally optimized lightweight detector to address these issues. On the data side, we present DefectGAN-SLS, a style-transfer-based generation network incorporating a self-light suppression branch to reduce overexposure artifacts and preserve structural details. Compared with the original DefectGAN, this improvement lowers the Fréchet Inception Distance (FID) score from 96.33 to 92.98, yielding more realistic synthetic samples. When used for model training, the inclusion of synthetic data improves detection performance. The mAP50:95 increases from 0.459 (real data only) to 0.514 with DefectGAN, and further to 0.523 with DefectGAN-SLS, particularly benefiting low-sample fault categories. On the model side, we introduce EFD-YOLOv11n, an enhanced detector based on YOLOv11n. It includes a cross-scale detection head to fuse shallow high-resolution and deep semantic features, alongside a lightweight attention module that suppresses redundancy and emphasizes key regions. A coarse-to-fine feature aggregation structure is also incorporated to improve multi-scale representation, enhancing localization accuracy for small and dense targets while maintaining real-time efficiency. Experiments show that EFD-YOLOv11n improves mAP50:95 by 3.8% (from 0.504 to 0.523) with nearly unchanged parameters (from 2.6 M to 2.9 M) and moderately increased FLOPs (from 6.3 G to 11.3 G), while maintaining a high inference speed of 416 FPS. This demonstrates an excellent efficiency–accuracy trade-off, making EFD-YOLOv11n a lightweight and practical solution for real-world EMU fault detection.

Source: Transportation Safety and Environment

DOI: 10.1093/tse/tdaf071

Authors: Ruixin Deng, Scarlett Liu, Yu Zeng

Institution: Central South University

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: $1,575