# A Rapid Bridge Crack Detection Method Based on Deep Learning
## 4 Discussion
Further research and technological advancements in the field of rapid crack detection technologies are instrumental for ensuring the long-term sustainability of bridge infrastructure and ensuring the safety of the public. Deep learning provides a new method for solving this problem with the rapid development of computer technology [52]. Firstly, drone technology can be used to sample the entire bridge [53]. This technique has the following advantages: (1) It is safe and reliable. The use of drones for inspecting bridges eliminates the need for manual inspection, avoids casualties, improves operational safety, and saves inspection costs. (2) The unmanned detection accuracy of bridges is high. The drone itself can carry high-definition cameras to take images of cracks. (3) It is more efficient to use drones for bridge inspection. Drone sampling technology is relatively mature. The proposal of collision-tolerant UAVs along with a two-stage inspection method for bridge coating was put forward by [PERSON] et al. [54]. [PERSON] et al. [55] extensively described the principles of drone-facilitated inspections as well as key factors to consider for optimal data collection. The images of the entire bridge captured by the UAV can be divided into many small-sized pictures and entered into a computer for crack detection. Even though rapid detection of cracks can be achieved using deep learning techniques, it still requires a large dataset to train the neural network. Additionally, it should be pointed out that the existing small sample training strategy is not sufficient to solve the issue of data scarcity [56]. In order to further reduce the cost of data acquisition and manual detection, the DCGAN can be employed to generate artificial crack samples from existing crack images. [PERSON] et al. [57] used the DCGAN to reconstruct the oil reservoir fracture model. The results showed that the reconstructed model could predict the pressure distribution accurately. Finally, YOLOv5 is used to carry out the detection process. YOLOv5 applies the adaptive anchor. Therefore, YOLOv5 learns the best anchor boxes in the dataset during the training process without having to run the K-means clustering algorithm offline to obtain the K anchor boxes and modify the head network parameters. Overall, the training process of YOLOv5 is simple and automated. Moreover, the Focus module is used by YOLOv5, which augments the channel count (the channel count has a minimal influence on the computation quantity) and reduces the dimensions of the input image without losing pixel information. As a result, the model calculation amount is greatly reduced [58]. This study has several limitations. The quality of the samples generated by the DCGAN needs to be improved, which is influenced by many factors like the size of the training dataset, the computing power of the computer, and hyperparameter optimization. During the training of the DCGAN, problems such as mode collapse and image collapse may also be encountered [38]. In future work, we will investigate more efficient DCGAN training strategies and achieve a higher-quality reconstruction of the cracks.