RSD-YOLO: An Improved YOLOv11n-Based Algorithm for Railway Surface Defect Detection
Abstract
Railway surface defect detection is a critical component of automated track maintenance and safety assurance. To improve the detection capability of lightweight YOLOv11n on rail surface images, this paper proposes RSD-YOLO, a conservative structural enhancement that adds a P2 high-resolution detection branch, replaces selected high-level C3k2 blocks with C2f modules, and introduces P2-SPPF context enhancement. The resulting detector changes the original three-scale P3/P4/P5 output to a four-scale P2/P3/P4/P5 output. Experiments on a four-class rail surface defect dataset show that RSD-YOLO achieves 0.7966 mAP@0.5 and 0.5616 mAP@0.5:0.95 on the test set, improving YOLOv11n by 8.05 and 7.24 percentage points, respectively.
Fields of Study & Subjects
Railway Engineering and Dynamics
Advanced Neural Network Applications
Infrastructure Maintenance and Monitoring
Publication details
- Journal / source
- Academic Journal of Emerging Technologies
- Publisher
- Erytis Publishing Limited
- Volume
- 3
- Issue
- 1
- Pages
- 93–93
- Publication date
- 22 May 2026