## 1 Introduction
In the case of the fine concrete structure environment, the hand-crafted feature representations above can acquire the discriminating image region feature sets. However, due to the complicated background changes, the artificially designed features may not well depict the cracks and backgrounds, which becomes one of the limiting factors of crack detecting performance. For instance, the LBP descriptor calculates the texture features of image region. Although the LBP model can perform well with the illumination challenge, it is unable to deal with unknown background noises. Therefore, it is preferable to learn a feature representation from the existing image data, rather than predefining a generic feature extraction model. After the image region feature extraction, the followed crack damage detecting process is designed to build one feature classification model. Specifically, the constructed feature classifier is utilized for recognizing the cracks among all the candidate image regions. Recently, some representative crack region classification algorithms have been advocated; [PERSON] et al. applied a SVM model for determining the optimal identification function between the crack images and non-crack ones [15]. [PERSON] et al., calculated the wavelet region features of concrete images, and then presented one bridge crack detecting method, based on SVM techniques [16]. In order to explore multitudinous crack damages, one binary-tree network using a SVM method has been proposed in [13]. An artificial neural network (ANN) is a computing framework, inspired by biological learning, which has been applied in fatigue life prediction [17], surface inspection [18], and in many other areas. In [19], the back propagation (BP)-based neural network classification method has been presented for detecting possible crack regions. As the training performance of the BP method is very slow, a varying slope of the activation function is advocated for training the crack region recognition model [20]. Additionally, the ensemble learning method can be also used for crack region identification. In [21], [PERSON] et al. combined multi-scale random decision forests and the wavelet transform for detecting potential crack regions. The above-mentioned crack region detecting models have obtained some satisfactory detecting results. However, the SVM-based crack detectors need to solve a quadratic programming problem and the ANN-based crack detectors are confronted with tedious iterative parameter tuning. Generally speaking, one concrete image should be separated into many small regions. Thus, for these crack region detection methods (including ensemble learning), the numerous image patches will involve a high computational burden. More importantly, considering the emergence of new crack and non-crack instances, it is necessary to update the crack region detector incrementally. However, these existing crack region classifications do not take into consideration this problem. Recently, deep learning (DL) models have gained significant attention, due to their successes in learning feature representation and classification, and thus, were also applied for surface defect detection [22; 23], face identification [24], crack damage detection, and so on. Through experimental results in [25], the convolutional neural network (CNN)-based crack detector has been proved to be better than the edge-based one. [PERSON] and [PERSON] et al. applied the multi-layer CNN technique for extracting crack damage features, and the fullly connected neural network is used as the final classification layer [26]. [PERSON] et al. identified the crack and no-crack patches by training one CNN model with a sliding window technique, which obtained much better performances than the traditional edge-based detections [27]. [PERSON] et al. combined the CNN model and a native Bayes data fusion strategy for detecting crack regions [28], and achieved superior performance, compared with their original LBP-based crack detection method [13]. [PERSON] et al.