Machine Learning Based Features Matching for Fatigue Crack Detection

The detection of fatigue cracks has gained widespread attention as it affects the safety of daily lives. Many crack detection methods have been developed up to now and perform well. However, there are still some problems left. It is hard to check the huge objects (such as bridge, building) by using vibration or ultrasonic sensor. Especially the inspection of bridges needs human inspectors. It is high cost, difficult access and dangerous. Vision based fatigue crack detection is considered as a good solution as the system can be carried by a drone and overcomes the above problem. In this study, FAST, ORB, SIFT and SURF features are employed to detect the fatigue cracks by feature matching and this study compares their performance. The experimental results show that ORB based feature matching gives the best result and it is an effective method to detect the fatigue crack.


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  • Accession Number: 01751129
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jul 28 2020 3:09PM