Abstract: Railway concrete sleepers are key safety-critical components in ballasted railway tracks. Due to frequent high-intensity

impact loadings from train-track interaction over irregularities together with hostile environmental conditions, complicated

characteristics of various crack patterns can incur on railway concrete sleepers, which will decrease their durability

and service life overtime. Early warning of those cracks can help railway engineers to plan and schedule for renewal and

maintenance timely and effectively. This study thus explores the artificial intelligence application of YOLOv5OBB (YOLOv5

with Oriented Bounding Box output) in the identification and classification of cracks in railway sleepers into three distinct

types: longitudinal, transverse, and inclined, based on their specific crack angles, which have not been investigated in

the past. The identification of crack angles is the novelty of this study. Recognising the various types of cracks is critical,

given their varying causes and degrees of severity. Current corrective maintenance methods pose considerable safety

risks to workers and exhibit low efficiency, underscoring the need for a more autonomous and efficient solution. This

study marks a significant stride towards revolutionising railway maintenance, evidenced by an impressive mAP (Mean

Average Precision) of 0.72 for crack detection and a 92% accuracy rate for angle detection. These promising results substantiate

our study’s potential to pioneer advancements in railway infrastructure maintenance.

AI-based technology to prognose and diagnose complex crack characteristics of railway concrete sleepers.pdf