Vision-based fatigue crack detection using global motion compensation and video feature tracking

Fatigue cracks that develop in civil infrastructure such as steel bridges due to repetitive loads pose a major threat to structural integrity. Despite being the most common practice for fatigue crack detection, human visual inspection is known to be labor intensive, time-consuming, and prone to error. In this study, a computer vision-based fatigue crack detection approach using a short video recorded under live loads by a moving consumer-grade camera is presented. The method detects fatigue crack by tracking surface motion and identifies the differential motion pattern caused by opening and closing of the fatigue crack. However, the global motion introduced by a moving camera in the recorded video is typically far greater than the actual motion associated with fatigue crack opening/closing, leading to false detection results. To overcome the challenge, global motion compensation (GMC) techniques are introduced to compensate for camera-induced movement. In particular, hierarchical model-based motion estimation is adopted for 2D videos with simple geometry and a new method is developed by extending the bundled camera paths approach for 3D videos with complex geometry. The proposed methodology is validated using two laboratory test setups for both in-plane and out-of-plane fatigue cracks. The results confirm the importance of motion compensation for both 2D and 3D videos and demonstrate the effectiveness of the proposed GMC methods as well as the subsequent crack detection algorithm.