# Identification of the Surface Cracks of Concrete Based on ResNet-18 Depth Residual Network
###### Abstract
In contrast to traditional binary image methods, this new algorithm allows for independent selection of specific cracks according to objective criteria and accurately captures the characteristics of fracture skeletons. On the other hand, deep learning-based approaches have been extensively employed in the field of road surface crack detection. Deep learning-based crack identification is a method that utilizes deep learning algorithms and artificial neural networks for crack detection and recognition. Researchers have developed convolutional neural networks and utilized them to automatically learn and extract data characteristics for the purpose of detecting concrete cracks [15; 16; 17]. In 2014, [PERSON] et al. [18] proposed an initial architecture for deep learning without increasing the amount of calculation. In 2016, [PERSON] et al. [19] were the first to employ deep learning algorithms for road crack detection, subsequently enhancing the network and demonstrating that convolutional neural networks outperform support vector machines in classifying crack images. The designed network structure exhibits relative simplicity; however, the sample scenarios of the training data lack comprehensiveness. This resulted in a slow final recognition speed and low accuracy. The pixel-level classification method was introduced by [PERSON] et al. [20], while [PERSON] et al. [21] further proposed a refined pixel-level multiple damage detection approach based on this, thereby enhancing its practical applicability. However, the detection time for images with excessively large pixel size is extended, leading to diminished efficiency. In recent years, significant progress has been made in crack detection through the application of deep learning-based crack recognition algorithms and convolutional neural networks. These advancements have largely benefited from breakthroughs in general deep learning algorithms. Researchers have proposed many kinds of models based on convolutional neural networks, which can adapt to different environments and achieve better testing results. Additionally, they employed a multi-sensor integrated image acquisition system to mitigate overfitting and enhance the accuracy of structural defect identification. [PERSON] et al. [22], [PERSON] [23], [PERSON] et al. [24], [PERSON] et al. [25], and [PERSON] et al. [26] proposed and refined crack detection methods based on the YOLO family of algorithms. The YOLO series represents an object detection algorithm that simplifies the task of detecting objects by transforming it into a single regression problem. This approach achieves real-time object detection through grid partitioning and regression of predictive boxes on the image. The YOLO series algorithms demonstrate exceptional real-time performance and detection speed, allowing for the simultaneous detection of multiple objects in images. This offers significant advantages in object detection. [PERSON] et al. [27] proposed an efficient mobile attention X-network, MA-Xnet, for crack detection based on two models, U-Net and Dual Attention Network (DANet). [PERSON] et al. [28] improved U-Net and proposed ECA-UNet, which has better recognition performance than the other two models in large-scale structural crack recognition. [PERSON] et al. [29] proposed a method for calculating the geometric features of cracks based on the concepts of skeleton extraction and function fitting. This approach aims to better capture the shape and orientation of cracks through the utilization of the Mask R-CNN algorithm for image refinement, making it suitable for both wider and narrower cracks. However, the model necessitates additional computational resources, including increased video memory and computing power, as well as a longer training period. [PERSON] et al. [30] employed an enhanced ResNet-50 as the underlying network architecture in their experiments to overcome the limitations of U-Net [31; 32; 33].