1. Research Background
Crack detection is of great significance in the fields of engineering and scientific research, mainly in preventing structural failure and extending service life, evaluating the strength, toughness and fatigue properties of new materials, and engineering research in complex and extreme environments.
Traditional crack detection can be done through metallographic microscopes, ultrasound, X-rays and other methods, but they all have varying degrees of defects in accuracy, efficiency and non-destructive testing.
The Qianyanlang research team has independently developed a crack detection method based on sparse optical flow and the Hungarian algorithm, which has the characteristics of high precision, high efficiency, and flexible adaptability.
2. Principle of the method
The sparse optical flow algorithm simulates cracks by interpolating and deforming the reference frame using the displacement information between images.
The deformation of the area is then calculated, and the crack area is extracted by difference calculation. Finally, the Hungarian algorithm is used to sort the time series cracks and complete the calculation of the crack endpoints and opening angles.
3. Method steps
The first step is to set the reference frame and calculation area
Select one or several frames before the crack occurs as the reference frame to ensure that there is no crack interference. Based on the reference frame, select a calculation area near the image crack that can cover the crack occurrence.
The second step is to calculate the displacement vector of each pixel in the image
By analyzing the changes in pixels between the reference frame and the current frame, the displacement vector ( dx, dy ) of each pixel is calculated. Let the time difference between the current frame and the reference frame be t, and the displacement vector (Δx, Δy ) of each pixel is calculated by sparse optical flow.
Where 
and 
represent the gradient of the image in the x and y directions respectively, 
indicating the change of the image on the time axis. The sparse optical flow algorithm solves the above equations to obtain the displacement vector of each pixel and extract the area with larger displacement.
Step 3: Interpolate the deformation reference frame
By bilinear interpolation or nearest neighbor interpolation, the pixel values of the reference frame are mapped to the new position to obtain the deformed reference frame.
where represents the pixel value of the reference frame.
Step 4: Crack extraction
The current frame and the deformed reference frame are interpolated and thresholded to extract the area with significant difference, i.e., the crack area.
Step 5: Sorting and calculating crack endpoints
The Hungarian algorithm is used to perform optimal bipartite matching of the crack timing information to obtain the crack points in different time frames. The coordinates of the crack endpoints are calculated by curve fitting method, and the angle between the endpoints is calculated by using the inverse tangent function.
Among them, (x1, y1) and (x2, y2) are the two endpoints of the crack, and θ is the opening angle of the crack.
4. Effect displa
5. Research summary
This paper introduces a crack detection method based on sparse optical flow and Hungarian algorithm. This method can intelligently, efficiently and accurately identify cracks in images by setting reference frames, calculating pixel displacement, interpolation deformation, difference calculation, crack extraction, crack sorting, and endpoint and opening angle calculation. This algorithm can be applied to industrial inspection, material monitoring and other fields to help ensure engineering safety, improve product quality, and promote the progress of materials science research.
