## 4 Experiments and Discussion
### Comparison in Training Efficiency  
As mentioned in Section 4.2, for improving crack damage detecting performances, plenty of training image patches were generated by an overlapping partitioned operation. The generated mass training image samples brought about a heavy computational load for obtaining the crack detector. In this work, we place emphasis on developing an effective and efficient crack detector. One novelty of our developed algorithm is the successful application of ELM in the multilayer crack region feature learning and classification. Compared with other learning frameworks (SVM or neural networks), the MECD model can obtain better generalization results with an efficient training speed, which will contribute to the application of proposed crack damage detection method. As these edge-based crack detection methods do not require the training step, only the crack detectors using local binary classification are discussed in this subsection. Specifically, the presented MECD method, the SVM-based one [11], and the DL-based one [26] are compared, in the aspect of training efficiency.  
Table 2 shows the training time of the compared crack detection methods, using the same amount of training samples. In addition, the software environment also has an effect on the training time of crack detector. In this paper, though all the compared models apply the MATLAB program, there are still several distinctions for implementing the crack defect detecting task. Here, the specific programming settings are shown at the bottom side of Table 2.  
As for the comparisons, with the utilization of the fast C-mex function, the SVM-based method can alleviate the high computational burden of the quadratic programming process. However, the SVM technique is a shallow classification model, and its corresponding result was not advantageous when compared with the multi-layer neural networks (i.e., DL and MECD). Among these compared models, the DL-based method applied the deep BP-based neural network for the image feature learning process and, thus, the total training process became very slow. For improving the calculating efficiency, the graphic processing unit (GPU) option was utilized. Despite all this, the DL-based method was still the most inefficient training method. By contrast, the MECD method was more efficient than DL-based one, which is due to the following two points: (1) The adopted ELM-AE feature learning could be quickly implemented with the APG method. (2) The ELM feature classification network did not need to be fine-tuned. Therefore, due to the two reasons above, the presented MECD model tended to achieve faster training performances than DL-based method.  
Moreover, for fair comparison, we attempt to implement the three crack detectors in the same software environment (MATLAB + GPU). Specifically, for the SVM-based crack detector, cuSVM  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **SVM1** & **SVM2** & **DL** & **MECD1** & **MECD2** \\ \hline Training time (second) & 243.6 & 34.6 & 1025 & 88.3 & **27.6** \\ Implementation & MATLAB + C & MATLAB + GPU & MATLAB + GPU & MATLAB & MATLAB + GPU \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training time and software environment of the crack damage detecting model.  
toolbox [42] was utilized for the binary SVM training task, and the corresponding training time was about 7 times faster (see Table 2) than that of implementation using the C-mex function. In addition, as for the proposed MECD model, the ELM classification was performed using the ELM-GPU toolbox [43], and the resultant training time was about 3 times faster (see Table 2) than that of the original MECD model. Through further comparison, one can see that the proposed MECD method was still the most efficient crack detecting model, and the corresponding damage detecting performance could also be guaranteed.