Crack detection and crack segmentation in concrete beams undergoing mode I fracture using computer vision and convolutional neural network

The study presents two crack detection techniques for a beam undergoing mode I fracture: (i) convolutional neural network approach and (ii) image processing using opensource computer vision library OpenCV and pixel count of cracks approach. The second method also includes crack segmentation and masking for visualization of the cracks. The study attempted to evaluate the accuracy of both methods at different crack mouth opening displacement of seven simply supported concrete beams being tested using three-point bend test. The novelty of the present work lies in quantification of the crack growth from captured images and evaluating the potential of applying computer vision techniques as a replacement for sensitive crack measuring instruments to create a robust computer vision based health monitoring system. Results suggest that both methods identified the cracks in the beam and are capable of generating a warning before the collapse.