# Automated Vision-Based Crack Detection on Concrete Surfaces Using Deep Learning
## 1 Introduction
Similarly, Bayesian algorithms were used to identify cracks in nuclear power plants, and deep learning segmentation algorithms were used to identify cracks in the tunnels [31]. Furthermore, deep convolutional neural networks (DCNNs) were recently explored for crack detection and classification [32, 33]. Most of the DCNNs focused on pixel-wise crack classification through semantic segmentation by associating each pixel [32, 34]. Deep learning networks require a large amount of training data and time. These can be minimized by fine-tuned pretrained DCNNs that use small amounts of data and provide reliable results in minimal time. Fine-tuned pretrained DCNNs such as AlexNet, GoogleNet, ResNet, SqueezeNet, and VGGNet have recently been used to detect and classify cracks in concrete structures. A VGG19 pretrained model was applied to create pixel-level crack maps on concrete pavements and walls [35]. Crack segmentations were performed using SegNet, U-Net, and ResNet models [36, 37, 38, 39]. A DenseNet-121-based fully convolutional network was studied to provide the pixel-level detection of multiple damages including cracks, spalling, efflorescence, and holes in concrete structures [40]. Furthermore, DCNNs based on VGG16 were also used for crack segmentation on the concrete surfaces [41]. All aforementioned deep learning approaches have shown promising performance in the crack detection of structures. Since the performance of DCNNs depends on various factors, such as data, filters, the number of layers, the number of epochs, and the network depth, it is difficult to select an appropriate pre-trained DCNN for crack detection with high precision and accuracy. The advantage of selecting an appropriate DCNNs is that it ensures better generalization and prevents overfitting. AlexNet, with many pre-trained DCNNs, is the most influential CNN widely applied to image classification and won the ImageNet LSVRC-2012 competition with a minor error rate of 15.3% [42]. The highlights of AlexNet are listed as follows: there are more filters in each layer; each convolutional layer is followed by a pooling layer; it uses ReLU instead of tanh, arctan, and logistic to add non-linearity that increases speed by up to 6x with the same accuracy; it uses a dropout layer instead of regularization to deal with overfitting; and it makes use of an overlap pooling layer to reduce the size of the network [43, 44]. These characteristics motivated the utilization of AlexNet in this study for crack detection and classification. This study utilized AlexNet, a pre-trained deep convolutional neural network, for the automated vision-based crack detection and classification. The proposed method consists of three steps: (1) collecting a large number of images from an open-source image dataset with subsequent categorization of two classes (no-crack and crack images); (2) developing a DCNN model, transferring the learning and augmentation process; and (3) automatically detecting and classifying the images using the trained deep learning model. Additionally, a cross-dataset study was performed to verify the ability of the trained AlexNet model. The precision, recall, accuracy, and F\({}_{1}\) metrics were used to evaluate the performance of the trained AlexNet model. The accuracy of the trained AlexNet model was further compared to other pretrained DCNNs such as GoogleNet, ResNet101, InceptionResNetw2, and VGG19.