# Concrete Crack Identification and Image Mosaic Based on Image Processing
## 1 Introduction  
Concrete is one of the most widely used engineering materials. Owing to its low cost, good durability, ease of shaping and other characteristics, concrete plays a role in widespread applications in construction engineering, traffic engineering, water conservancy engineering, and other infrastructure construction efforts. However, with the coming maintenance period of the engineering structures built in the early years, concrete structures inevitably suffer damage and destruction [1, 2] under the long-term actions of freezing-thawing cycles and loading. This degradation first manifests as structural cracks whose size and direction are important factors to evaluate structural reliability. After a crack occurs in a structure, moisture enters the concrete beam through the crack, causing the steel to rust. The expansion of water into ice saggravate the development of cracks when the external temperature is low. The direction of the crack allows structural engineers and relevant management personnel toevaluate the structural condition of the crack and predict its development. Therefore, the safe operation of engineering structures is often evaluated based on cracks. At present, cracks on concrete structures are mainly inspected by the manual visual observation of the surface. For example, in bridge crack detection, a large machine such as a test vehicle is usually used to position the inspector underneath the bridge, and the cracks are identified by the naked eye and measured by an optical microscope. It is precisely because of this measurement characteristic that the detection accuracy of cracks largely depends on the proficiency of the detection personnel. For many structures, the inspector cannot go to the testing location. Therefore, this manual detection method is inefficient, costly, and has security risks. With the rapid development of signal processing theory and modern computer technology, machine vision [3; 4; 5; 6; 7; 8] has played an increasingly important role in crack detection. Image processing technology can be used to safely and efficiently extract crack information. Crack information extraction mainly includes the identification of the crack size [9; 10; 11; 12] and the final stitching [13; 14; 15; 16; 17] of the crack image. [PERSON] et al. [18] compared four edge detection algorithms, the fast Haar transform (FHT), the fast Fourier transform, and the Sobel and Canny algorithms, and their results showed that the FHT had the best performance in crack detection. [PERSON] et al. [19] developed CrackTree, a fully automatic method for detecting crack curves from a pavement image, and proposed a new geodetic shadow-removal algorithm for eliminating road shadows and enhancing the contrast of cracks. [PERSON] et al. [20] proposed the FoSA, or F* (pronounced F star) seed growing approach, for pavement crack line detection, which is based on crack seed growing strategies and solved the problem of automatic selection of the start and end points of the F* program. This method has a strong anti-speckle-noise capability for extracting crack lines from pavement images. [PERSON] et al. [21] designed a spatial demodulation robust multi-feature classifier for the crack detection of bridges, which is a detection method based on machine learning and the integration of multiple features. Experiments have shown that the classification accuracy of this method is high. [PERSON] et al. [22] used multiple filters to process crack images and proposed a new image processing method to detect cracks. [PERSON] [23] investigated CrackForest, a road crack detection framework based on a random structure forest. They used a new crack descriptor to more effectively identify cracks in a noisy environment. [PERSON] et al.