# Identification of the Surface Cracks of Concrete Based on ResNet-18 Depth Residual Network
###### Abstract
These limitations include suboptimal crack segmentation performance and difficulties in identifying narrow cracks, while also improving inference speed compared to the standard U-Net. [PERSON] et al. [34] acknowledged the limitations of ResNet-34 in handling intricate data relationships and proposed GM-ResNet, a new ResNet with favorable evaluation metrics. ResNet is a widely adopted deep convolutional neural network model that incorporates residual connections to effectively capture intricate image features. This approach addresses the issue of vanishing gradients commonly encountered in conventional deep networks. The residual learning approach facilitates the smooth flow of information across network layers by introducing cross-layer connectivity. This enhances the trainability and optimization capabilities of the network, ultimately improving its overall performance. The ResNet models commonly utilize deeper network architectures, which enable the extraction of more complex feature representations and improve the model's accuracy. In the field of crack detection, researchers have developed new models based on the ResNet module, which have yielded remarkable outcomes. [PERSON] et al. [35] proposed a detection method for bridge pavement crack detection based on the InceptionResNet-v2 [36] module. This method can capture multi-scale features of cracks, improving efficiency without pre-training. [PERSON] et al. [37] proposed a method that utilizes the principles of Pixel Intensity Similarity Measurement (PIRM) to address the issue of noise and to detect cracks. They employed VGG-16, ResNet-50, and InceptionResNet-v2 models for this purpose. The experimental results demonstrate that the ResNet-50 model exhibits superior suitability across all categories of noisy images. [PERSON] et al. [38] proposed RUC-net, an innovative architecture that integrates UNet and ResNet. They introduced the spatial channel squeeze and excitation (scSE) attention module [39; 40; 41; 42] while utilizing the focal loss function. The approach aims to attenuate the less relevant information and address the issue of classification imbalance, in order to optimize the crack detection method. [PERSON] et al. [43] employed the ResNet model to detect cracks and found that ResNet-50 achieved higher accuracy compared to ResNet-34. This indicates the superior performance of deep structural frameworks in identifying tunnel defects. The field of engineering places a greater emphasis on the precision of identifying cracks, making the enhancement of identification rate more valuable. The study utilizes the ResNet-18 model due to its comparable performance with ResNet-50, while also minimizing computational and memory requirements. However, it is important to exercise caution when utilizing ResNet-18 with small datasets, as it has a higher susceptibility to overfitting compared to ResNet-50. The accuracy and efficiency of model detection can be improved by creating a dataset using TensorFlow and optimizing the model with the Adam optimization algorithm. However, the previously mentioned methods still have certain limitations. For example, when dealing with complex backgrounds in images of cracks, it becomes unachievable to simultaneously identify and classify cracks in concrete imagery. In addition, the algorithm employed in those studies was implemented at an early stage, and recognition accuracy and efficiency of image processing were found to be relatively low. Therefore, it is crucial to investigate an efficient deep neural network algorithm with reduced training time for accurate identification of cracks in concrete structures and effective discrimination of interference targets in complex scenarios. Simultaneously, the network model should possess lightweight characteristics to ensure stable operation on unmanned platforms. In this paper, an image recognition method based on the ResNet-18 deep convolutional neural network is proposed, which realizes the recognition of concrete cracks and the detection and classification of crack types.