## 1 Introduction  
Concrete structures play a predominant role in civil construction. Owing to internal and external factors, crack damage will inevitably occur in concrete structures, and crack defects are the main reasons for the reduction of bearing capacity, durability, and waterproofing of concrete structures. Therefore, studying the detection methods of concrete crack damage is of great importance for the safety assessment of concrete structures, the prediction of service life, and the resistance of natural disasters. Researchers have presented many methods to detect concrete cracks. The readers can refer to one review article [1], which discussed the current practices and emerging techniques for pavement distress detection. For the crack damage areas, the pixel value is distinct from those of the background contents, and could be seen as a demarcation line of the concrete image. As a result, several crack damage detecting methods using global analysis have been presented. [PERSON] et al. applied four edge detectors for finding the concrete cracks, and a fast Haar transform was identified as the best solution [2]. In [3], curvelet transform is utilized for detecting the void diseases in ballast-less track, which may result in the cracks of the track slab. [PERSON] developed one Canny edge-based crack detection, which utilized a semi-automatic threshold value [4]. With the operation of empirical mode decomposition, a Sobel operator was applied for detecting the crack regions in [5]. However, only fifteen simple images were used in their experimental results, which is not suitable for the complexity of typical backgrounds. [PERSON] et al. also presented one inspection method for concrete surface cracks using terrestrial laser scanning [6]. In [7], image preprocessing (transforms, filters, edge detector, and so on) was applied for addressing the background noises, and then the crack areas were detected by a decision tree. In addition, similar to the edge-based crack detection methods, an Otsu based algorithm was exploited for segmenting the crack regions from the backgrounds [8]. Based on the Canny detecting results, [PERSON] et al. applied the K-means algorithm for exploring the crack regions [9]. [PERSON] et al. utilized one adaptive threshold strategy for preliminary crack segmentation, which can remove most of the background content [10]. However, in practice, the gray scales of an identical crack region may vary widely, in terms of non-uniform illuminations or other background disturbances, and perhaps the corresponding crack detecting results were bad. To solve the problem, the local analysis based crack detection model is proposed. Generally, by dividing the raw image, many image patches can be first obtained. Then, the two-class classifier is applied for determining the crack regions. Usually, the crack detection methods by local analysis are made up of two parts--a feature extracting process and crack damage region identification. Through the contribution of advantageous feature presentation and a powerful classification technique, the local analysis-based crack detectors have achieved better performances than the general crack detectors based on global analysis. Recently, many works have used different region feature presentations or feature classification techniques. For the image region feature representation, the mean value and variance value of image patches were calculated by [PERSON] [11]. Similarly, the moment feature extraction was utilized for detecting the crack regions in [12]. These region features, mentioned above, are simple and easily affected by background shadows. For coping with this illumination challenge, [PERSON] et al. exploited the local binary patterns (LBP) model for extracting the region features of concrete images [13]. Additionally, the histogram features of image patches were computed in [14], which can improve the crack detecting performances.