# Concrete Crack Identification and Image Mosaic Based on Image Processing
## 1 Introduction
[24] presented a crack identification process using a hybrid image processing strategy with an unmanned aerial vehicle (UAV) that utilized two sets of optimal parameters, \(P_{w}\) (optimal parameters minimizing estimation errors in crack width) and \(P_{l}\) (optimal parameters minimizing estimation errors in crack length), to accurately detect the crack width and minimize the loss of length. [PERSON] et al. [25] introduced the applications of a UAV in crack identification with a deep learning algorithm. The algorithm was adopted to effectively classify and locate images, and the identified cracks were automatically visualized on an inspection map using location matching. Though these methods have certain advantages in various fields, some problems remain. First, when acquiring images, experimenters need to calibrate the camera's unit pixel size [26; 27] in a very cumbersome way, such as the scale method [28] of positioning rulers at the crack position, which is not applicable to all bridge types. Relatively speaking, the object distance method does not require cumbersome calibration, the operation is simple, and it is suitable for almost all bridge types. However, there is no uniform standard for the calculation of crack size. Second, most current methods for calculating crack length refine the crack into a single pixel skeleton along the direction of the crack and then calculate it by counting the number of pixels. However, these methods heavily rely on the choice of threshold for image binarization. When the threshold value is large, the ends of the cracks and some small cracks are often ignored, which can result in missing pixels, making the calculated value smaller than the actual length. When the threshold value is selected to be small, the crack is wide, and a large amount of noise is present, the problem of pixel redundancy occurs, resulting in a calculated result that is larger than the actual value. Third, most image stitching algorithms [29; 30] tend to apply point-to-point matching when feature points are matched. There is no correlation between feature points, which often causes mismatching. Then, the mismatched points should be eliminated, which results in low computational efficiency. To the authors' knowledge, the associated researchers did not check the stitching accuracy after they finished image stitching. In this paper, a crack information extraction method is proposed for length calculation and image mosaic, and a data acquisition system with a camera and a laser range finder was built to provide the crack image and working distance needed for data processing. Some main aspects of the work are as follows:  
* The object distance method and the scale method were compared for the aim of estimating the crack width, and the results showed that the object distance method was more efficient than the ruler method. * The method of \"double edge skeleton method\" was adopted after analyzing the disadvantages of common crack length calculation methods. First, the edge information of the crack was preserved as much as possible by setting the appropriate threshold. Then, the number of pixels in the edge of the crack was calculated by region. Third, redundant pixels with wide cracks and large amounts of noise were removed, and the missing pixels of narrow cracks were added. This method greatly improved the calculation accuracy. * The scale-invariant feature transform (SIFT) algorithm was used and improved to correlate and compare feature points in the image mosaic. The improved algorithm could achieve the priority matching of feature points, thereby improving the splicing efficiency. After the image mosaic was created, the accuracy was verified by comparing the mosaic image with the actual structure.