Work

Fuzzy‐YOLO Model for Rail Anomaly Detection: Robustness Under Limited Sample and Interference Conditions

Year: 2025

Type: article

Abstract: ABSTRACT Accurate detection of surface anomalies in railway tracks is critical for ensuring train operation safety and enabling intelligent railway management. However, the scarcity and pronounced imbalance of anomaly samples significantly constrain model training and generalisation. Moreover, complex environmental factors such as illumination variability, sensor noise, and motion blur pose additional challenges to model robustness in real‐world applications. This study presents a Fuzzy‐YOLO model tailored for limited sample datasets. Built upon YOLOv11, Fuzzy‐YOLO incorporates a fuzzy‐non‐maximum suppression (NMS) mechanism and integrates a lightweight fuzzy residual neural network (RFNN‐Res) module based on fuzzy logic for anomaly classification. The final anomaly type is determined via a weighted voting strategy. Experimental evaluations demonstrate that Fuzzy‐YOLO achieves a mean average precision (mAP) of 98.90%, exhibiting notably enhanced stability compared to YOLOv11 under conditions of varying illumination, noise, and motion‐induced blur.

Source: IET Image Processing

DOI: 10.1049/ipr2.70156

Authors: Liyuan Yang, Ming Yang, Ghazali Osman, Safawi Abdul Rahman, Muhammad Firdaus Mustapha

Institutions: Kunming University, Universiti Teknologi MARA System, Universiti Teknologi MARA

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,740