Studying the performance of YOLOv11 incorporating DHSA BRA and PPA modules in railway track fasteners defect detection

Studying the performance of YOLOv11 incorporating DHSA BRA and PPA modules in railway track fasteners defect detection

Abstract With the development of railway transportation and the advancement of deep learning, object detection algorithms are increasingly replacing manual inspection of track fasteners. However, current algorithms struggle with low accuracy in complex weather conditions or low-contrast backgrounds....

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Bibliographic Details
Main Authors: Chengwei Zhang, Jiawei Zhu, Yihao Ma, Qingmei Huang
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Defect detection
Deep learning
YOLO11
Feature fusion
Attention mechanism
Online Access:https://doi.org/10.1038/s41598-025-13435-z
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https://doi.org/10.1038/s41598-025-13435-z

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