Work

Train-YOLO: An Efficient and Lightweight Network Model for Train Component Damage Detection

Year: 2025

Type: article

Abstract: Currently, train component fault detection is predominantly carried out through manual inspection, a process that is inefficient, prone to high omission rates, and carries safety risks. This study proposes an innovative fault detection model for train components based on YOLOv8, aiming to overcome the inefficiencies and high omission rates associated with traditional manual methods. By optimizing the YOLOv8 network architecture and integrating the ADown module, C2f-Rep, and DHD, the model significantly improves computational efficiency and detection accuracy. Experimental results demonstrate that the optimized Train-YOLO model achieves a peak accuracy of 92.9% in train component fault detection. Additionally, it features a smaller model size and reduced computational demands, making it ideal for rapid on-site deployment. A comparison with other leading detection models further highlights the superiority of Train-YOLO in both accuracy and lightweight design.

Source: Sensors

DOI: 10.3390/s25164953

Authors: Hanqing Zong, Ying Jiang, Xinghuai Huang

Institutions: Nanjing Forestry University, Southeast University

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: $3,137