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Defects on the rail surface will hasten the wear of the wheels. At the same time, when the wheel is periodically hitting and rolling surface defected, the defects will gradually develop into the interior, which significantly increases the possibility of a train derailment and serious safety accidents. A timely checking of the railway tracks to find defects as early as possible is a basic condition for ensuring the safety of railway operations. It also prolongs the service life of railways because most of the rolling contact fatigue (RCF) can be eliminated during rail grinding. Such defects appear as spalling and cracks in the first stage of the rail surface. Manual detection has been challenging to meet the large-scale railway running mileage. Therefore, a more efficient automatic detection method is indispensable. This article reviews the latest research and exploration on the defect inspection of rail surfaces in recent years. In the article, not only the application of traditional ultrasonic and acceleration detection methods but also contributing computer vision and deep learning to detect defects on the rail surface. The new detection technology can even classify and evaluate the damage, further improving the efficiency of the detection system. Besides, the emerging research on defect state prediction to reduce inspection costs is interesting.
Lei Kou (Sat,) studied this question.