## 4 Experiments and Discussion
### Qualitative Evaluation  
In this subsection, some representative crack region detection results are shown in Figure 7. Specifically, the 100 concrete test images were divided into three types: Dataset 1, which contained cracks and background disturbances; Dataset 2, which had cracks and illumination changes; and Dataset 3, which contained cracks and image blurring. Moreover, some additional concrete images constituted Dataset 4, which contained some background noises but did not involve any crack damage region. For simplicity, these testing datasets, mentioned above, are named DS1, DS2, DS3, and DS4, respectively. For better comparison, the ground truth of each concrete image is shown in the second column of the figure. The right-hand five columns show the crack damage detecting results of Canny [4], Otsu [8], SVM [11], DL [26], and the advocated multilayer ELM-based crack detector (named to be the MECD model), respectively. The specific performance discussions are as follows. (1) Background Disturbances  
For dealing with the images from DS1, one can see whether the crack detection methods can tackle the disturbances in a complex environment (e.g., pockmark, attachment, or crack-like). As for the Canny-based method, some tiny background noises can be filtered using the Gaussian image filtering process. There are still several blocky background mistakes (see Figure 7(1-2)), however, and, as their areas and visual parameters are unknown, it is impossible to remove them by using naive post-processing methods. The Otsu-based method can perform clustering-based image segmentation automatically. However, in some cases, the pixel gray values of local concrete image regions are close to those of crack defects. For instance, in Figure 7(1), the attachments nearby the true crack damages are easily mistaken using the Otsu based model. The post-morphological processing can delete some mistaken background noises, but these visual parameters of mistaken identified areas are indeterminate and, thus, the resultant crack detection results were not satisfactory. From the illustrations in Figure 7(3), it can be seen that stripes are easily mistaken for crack damages, using the SVM-based method. A possible reason may be that it only adopts naive image region features and, consequently, the subsequent SVM classification model cannot obtain a robust hyperplane between crack samples and non-crack ones. For the DL based crack damage detection model, thank to the strong feature learning capabilities of the multi-layer network, the attachment or pockmark disturbances (see Figure 7(2-3)) can be well addressed. However, the whole crack regions may not be recognized by a DL-based crack detector. For instance, in Figure 7(3), the in-between regions of crack objects are mistakenly identified as non-cracks. A possible reason is that the visual features of the undetected crack areas are close to those of some of the background troubles (e.g., the stripes shown in Figure 7(1)). However, the discrimination between the ambiguous potential crack regions and background disturbances is not good enough in the DL-based crack detector. From the compared performances above, it is clear that the presented MECD model has obtained satisfactory detecting results, reasoning by the following two points: (1) The developed multi-layer sparse feature representation can extract the high-level region features of image patches--comparedwith the existing DL-based crack detection model, ELM theory has already proved that the random feature projection can satisfy the universal approximation capability [31], and, thus, more important contents of crack image regions can be extracted for hidden-layer feature learning, which can well deal with complicated background noises. (2) An accurate ELM-based crack region identification model was exploited for the ultimate pattern decision of image patches. With ELM theory [31], high-dimensional non-linear mappings of obtained high-level features can offer a satisfactory feature classification precision. 2.