RC Bridge Concrete Surface Cracks and Bug-Holes Detection Using Smartphone Images Based on Flood-Filling Noise Reduction Algorithm  
Haimin Qian  
camera [24; 25], UAV (unmanned aerial vehicle) [26; 27; 28], and smart mobile phones [29; 30]. It is of great importance to design or develop a simple and convenient detection method for concrete cracks to guide decisions on structural safety and reliability, such as designing a technology for smartphone or other mobile terminal-based participation in the life-cycle inspection of bridge infrastructure concrete cracks [31; 32; 33]. However, crack and bug-holes detection using a concrete image is well known to be difficult because of some manual traces or other reasons such as background noises of the image, irregularly illuminated conditions, shading and a blemish, and the background noises of the image, which are important interference factors [34; 35]. Thus, noise reduction is a key process in digital image detection technology for concrete cracks.  
Several algorithms were proposed during the image pre-processing to reduce or remove noises in the image, such as mean filtering and median filtering [36; 37; 38]. The method of mean filtering is based on the assumption that the spatial correlation between adjacent pixels in the image to be processed is extremely high, and that the pixel gray values are less abrupt, while noise exists independently. In this way, after the gray value of a pixel is replaced with the gray mean of adjacent pixels in its neighborhood, the purpose of smoothing the image and filtering the noise can be achieved. The median filtering needs to sort the gray values of the pixels in the neighborhood template, and then the sliding template used by the median filter generally contains an odd number of pixels. When traversing the image, the gray value of the pixels in each template is sorted from small to large, and the median value of the gray scale is taken instead of the gray value of the pixels in the center of the template. The effect of the median filter largely depends on the choice of the filter window size. A small filter window size can protect some details in the image, but the denoising effect is not notable; the filter window size has a good noise-filtering effect, but it will cause the image feature boundary to blur. So, it is a key step to look for a good noise reduction algorithm in image processing, especially for cracks and bug-holes detection.  
The flood fill algorithm is another denoise algorithm. It uses the concept of water always flowing from a higher elevation to a lower one [39]. The flood fill algorithm is an algorithm to simulate the fluid diffusion process, which is widely used in the image processing process of determining connected regions in a multidimensional space [40; 41; 42]. In the flood fill algorithm, the thresholding method was utilized to avoid the initial seed pixels being planted manually so the denoising point could be presented [43]. The threshold range value of the flood fill algorithm has a direct impact on the noise reduction effect. There are few studies in this area, especially for the image detection of cracks and bug-holes in concrete surfaces.  
So, in order to explore the suitable noise reduction algorithm for the image detection of cracks and bug-holes in concrete surfaces, an experiment on the flood-filling noise reduction algorithm was conducted in this study. Firstly, the threshold range of the flood-filling noise reduction algorithm was investigated experimentally. Then, surface cracks and bug-holes of NC bridge concrete were detected using smart terminal images based on the flood-filling noise reduction algorithm. The aim of this study was to determine the threshold range for the detection of cracks and bug-holes in order to improve the detection accuracy.