# Structural Crack Detection and Recognition Based on Deep Learning
## 5 Conclusions  
Three kinds of neural networks are used to recognize crack images. In this experiment, we can see that ResNet18 is better than the other two neural networks. In the real world, the types of crack images will be very rich. How to better extract features from images and accurately recognize cracks is difficult. We can see from the structure of the model that the structure of AlexNet is relatively simple, and there are few convolutional layers, the depth of the model is relatively shallow, while the convolutional layer of VGGNet13 is more, and the depth of the model is also deeper, and its performance is better than AlexNet. It can be seen that the deepening of the neural network can better extract the features in images, but when the model is blindly deepened, the performance may not always be good because the gradient vanishes during optimization, which causes the neural network to fail to converge. Therefore, ResNet18 solves the vanishing gradient problem. It can be seen in the model diagram that it is a short connection so that during optimization, the gradient is propagated back and there is no gradient vanish, which makes the neural network easy to converge. Therefore, in practical applications, ResNet18 has a deeper depth and short connection, so the features in the image can be better extracted and cracks can be recognized.  
In the second part of the study, we mainly trained the YOLOv3 model for crack target detection on crack images. We can clearly see from the loss curve that the model finally converged, but in the IOU curve, we can see that it oscillates afterwards; that is, the prediction box and the real box never overlap perfectly. The reason may be that when the training dataset is built, there will be a small amount of overlap between the labelled box when the crack image is labelled (refer to Figure 2). However, in the actual video and picture test, the model performance is still good, and the crack area in the picture and video can be accurately detected. The disadvantage is that the detection box has a small overlap, which is also part of later research.  
In practice, the method in this article can be extended to real-world structures to realize more effective and reliable monitoring. For example, drones are used to carry cameras to quickly take videos and images related to roads and bridges (concrete structures), wireless transmission is used to the local end, and then, the method in this article is used to input the images and videos into the model. ResNet18 can quickly recognize cracked images and then use the YOLOv3 model to detect the crack area of the crack images and video. The trained model can accurately detect and recognize the crack area. In addition, thanks to the advantages of YOLOv3 in target detection speed, the method proposed in this paper can meet the requirements of crack detection speed. There are broad application prospects in the real-time detection of cracks.  
Figure 13: Video test.  
**Author Contributions:** Conceptualization, C.Y., J.C., and Z.L.; methodology, C.Y. and J.C.; software, C.Y.; validation, C.Y., J.C., and Z.L.; formal analysis, C.Y.; investigation, C.Y.; resources, Z.L.; data curation, C.Y.; writing--original draft preparation, C.Y. and J.C.; writing--review and editing, Z.L. and Y.H.; visualization, C.Y.; supervision, J.C.; project administration, Z.L.; funding acquisition, J.C., Z.L., and Y.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported financially by the National Key Research and Development Program of China (Grant No. 2017 YFE0111400).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors thank [PERSON] and [PERSON] for their assistances in collecting pictures.  
**Conflicts of Interest:** The authors declare no conflict of interest.