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Intelligent Identification Algorithm for Typical Rail Damage Based on Improved YOLOv8
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Hou-xue XIANG1, Gui-yang XU1, *, Yu-hua ZHANG2, Xiao-yan HUANG2
Science Technology and Engineering | 2025, 25(18) : 7785 - 7792
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Science Technology and Engineering | 2025, 25(18): 7785-7792
• Papers·Traffics and Transportations •
Intelligent Identification Algorithm for Typical Rail Damage Based on Improved YOLOv8
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Hou-xue XIANG1, Gui-yang XU1, *, Yu-hua ZHANG2, Xiao-yan HUANG2
Affiliations
  • 1 School of Mechanical, Electrical and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102612, China
  • 2 Infrastructure Testing Research Institute of China Academy of Railway Sciences,Beijing 100081, China
Published: 2025-06-28 doi: 10.12404/j.issn.1671-1815.2404963
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The internal damage of the steel rail is serious, but the non-destructive testing B-display detection image has a lot of noise and noise, and the spatiotemporal distribution characteristics of different damages are not obvious, making it difficult to effectively identify. In response to this situation, a rail screw hole crack B-image recognition algorithm based on improved YOLOv8 was studied to improve the accuracy of intelligent identification of rail damage. Firstly, to reduce the missed detection of small damage targets, RepHGNetv2 network was used to optimize the YOLOv8 backbone network and improve the detection recall rate. Then, in order to improve the adaptability of the model to different types of damage detection, the detection head of YOLOv8 was replaced with Effientnet to improve the detection accuracy of the model. Finally, the LSKA attention mechanism was introduced into the SPPF module to enhance the model’s anti-interference ability against noise signals and improve its accuracy. The actual line detection results have verified that the detection accuracy of the above model reaches 95.1%, the recall rate reaches 93.8%, and the average accuracy reaches 97.6%, which is improved compared to other commonly used algorithms.

rail flaw detection  /  B-imaging  /  damage detection  /  YOLOv8  /  bolt hole crack
Hou-xue XIANG, Gui-yang XU, Yu-hua ZHANG, Xiao-yan HUANG. Intelligent Identification Algorithm for Typical Rail Damage Based on Improved YOLOv8[J]. Science Technology and Engineering, 2025 , 25 (18) : 7785 -7792 . DOI: 10.12404/j.issn.1671-1815.2404963
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Year 2025 volume 25 Issue 18
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Article Info
doi: 10.12404/j.issn.1671-1815.2404963
  • Receive Date：2024-07-03
  • Online Date：2025-12-17
  • Published：2025-06-28
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  • Received：2024-07-03
  • Revised：2025-03-19
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Affiliations
    1 School of Mechanical, Electrical and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102612, China
    2 Infrastructure Testing Research Institute of China Academy of Railway Sciences,Beijing 100081, China
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表12种不同金属材料的力学参数
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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