## 2 Literature Review  
Sometimes, some research work has been done based on transfer learning for various types of crack detection. In [12], the authors presented a practical deep-learning-based crack detection model for three types of crack images, including concrete pavement, asphalt pavement, and bridge deck cracks. Also, they proposed an encoder-decoder structural model with a fully convolutional neural network, named PCSN, also known as SegNet, and achieved 99% accuracy. In [13], the MobileNet-based transfer learning model is proposed for wall crack detection as well as authors got 99.59% accuracy. In [14], a transfer learning approach was applied to the VGG16 model to recognize structural damage and achieved 90% accuracy. In [15], a deep transfer learning method based on YOLOv3 and RetinaNet models, pre-trained on the COCO dataset, was proposed for detecting rail surface cracks. In 2021, a novel method, FF-BLS, was proposed that could accurately classify crack and non-crack images with an accuracy of 96.72% [16]. In 2019, a transfer learning approach on the VGG16 pre-trained model achieved 94% accuracy, and slightly fine-tuning a well-trained FC layer with VGG16 achieved 98% accuracy [17]. In 2018, a transfer learning method on the VGG16 pre-trained model gained 92.27% accuracy after training with less than 3500 images [18]. They conclude the transfer learning approach is suitable to train on limited datasets. In [19], an image segmentation model based on ResNet101 was proposed for concrete crack detection with 94.52% precision and 95.25% recall accuracy in 2021. In 2021, a deep learning model was proposed, and although they achievedgood accuracy on training, validation accuracy was 97.7% for the CCIC [20] dataset [21]. A deep, fully convolutional neural network named CrackSegNet with dilated convolution, spatial pyramid pooling, and skip connection modules was proposed to detect concrete cracks in tunnels [22]. In 2021, three kinds of deep neural networks, AlexNet, ResNet18, and VGGNet13, were compared and found ResNet18 (accuracy 98.8%) performs well compared with the remaining two models [23]. In 2021, the transfer learning method was applied on GoogLeNet Inception V3 and CNN-Crack; GoogLeNet Inception V3 performs well compared to the other one with an accuracy of 97.3% on drone cameras [24]. In [25], vision-based metal crack detection was proposed. In 2021, a pavement crack detection method based on the YOLOv5 model was proposed with 88.1% accuracy [26]. Recently in 2021, a railway slab crack detection method was proposed based on the VGG16 model and achieved 81.84%, 67.68%, and 84.55% in precision, IoU, and F1-score, respectively [27]. In [28], a deep learning model was developed for ceramic crack segmentation. In [29], a concrete air voids detection and segmentation method was proposed based on the path aggregation network (PANet). In [30], a thermal crack detection method, U-Net, was proposed to be used in fire-exposed concrete structures and achieved 78.12% Intersection over Union (IoU). In [31], a dilated convolution with Resnet-18 as the basic network model was proposed for detecting concrete cracks. In [32], a concrete crack detection method using a U-net fully convolutional network was proposed in this paper.  
In this experiment, we built a concrete cracks detection model using the transfer learning (TL) approach on various well-known CNN models. We applied four available CNN models named VGG16, ResNet18, DenseNet161, and AlexNet with pre-trained (trained on ImageNet) weights for utilizing the transfer learning approach. TL-based CNN approach achieves over 99% accuracy for all the models. Our approach fits well with AlexNet most, and its performance outweighs others. AlexNet achieves 99.90% accuracy on the validation set after only 13 epochs of training. The training duration provides better timing over VGG16, ResNet18, and DenseNet161 models.