## 1 Introduction  
Concrete cracks are a common indication of concrete defects in civil engineering structures. It affects structural health and induces an additional risk of unexpected breakdowns and accidents [1, 2]. Hence, detecting cracks regularly and taking appropriate actions for the safety of the concrete structure is of great significance. The traditional method of detecting cracks on concrete structures relies on professional observation. Such conventional methods are not only costly but also laborious, time-consuming, and on many occasions, dangerous too [3]. However, the recent advancement of machine learning-based image processing technologies has gained attention as an efficient and automated method to detect cracks on concrete structures that also overcome the cons of manual methods [4].  
The growing computer vision community working on different image-detection methods. It has proposed many techniques over the decades, including thresholding [5], edge detection [6], and wavelet transforms [7], to name a few, where some of these methods address concrete crack detection problems. However, this field needs an efficient and reliable solution as the concrete images are challenged with various surface textures, irregularity of cracks, and background complexity that differs crack detection from other image analysis applications.  
Recently, deep learning-based models, predominantly neural networks with multiple layers, are playing a significantly successful role in feature learning [8]. On top of that, the availability of high-performing computing facilities and ongoing improvement of excellent training methods on available datasets drive the rapid development of deep learning. In this saga, the convolutional neural network (CNN) is a feed-forward neural network that performs excellently in large-scale image processing [9; 10]. Although some of these models proved excellent in feature extractions on different applications, their accuracy requires improvement for concrete crack detection. To achieve efficient performance, reduce training time, and overcome the lack of a humongous dataset, in this paper, we have proposed a method of transfer learning-based CNN with the pre-trained model that shows significant improvement [11]. Our work has made the following contribution:  
* We conduct experiments on the CCIC dataset with four different CNN models (VGG16, ResNet18, DenseNet161, and AlexNet), applying the transfer learning technique for detecting concrete surface cracks from images and examination with other models to demonstrate the success of the suggested model.
* We designed our model such that every CNN model has only one fully connected (FC) layer, having two output features for binary classification. We modified the VGG16 and AlexNet models by replacing the last three FC layers with only one FC layer.
* Our strategy is the most compatible with AlexNet, and it outperforms the competition. AlexNet achieves 99.90% accuracy on the validation set on the CCIC dataset.
* The proposed method demonstrates superior crack detection for concrete structures, which can efficiently be utilized for other detection purposes.  
The paper is now organized as follows. We provided a brief review of the literature in Section 2. Section 3 includes methodology, which describes experimental Setup, Model Training, and Evaluation. Section 4 presents the result analysis and discussions. The paper is concluded in Section 5.