# Deep Transfer Learning Approach for Identifying Slope Surface Cracks
## 4 Discussion
### Future Work  
1. Due to the lack of image data of surface cracks in soil and rock masses, there is little research work focusing on the deep learning of surface cracks in soil and rock masses. Therefore, it is very important to collect the image data of surface cracks in soil and rock masses and establish a dataset of surface cracks in soil and rock masses. In the future, UAV aerial surveys can be used to collect crack images in the study area, and image cropping can be carried out through relevant codes to establish a dataset of surface cracks in soil and rock masses in the study area. In addition, the dataset sharing network platform of surface cracks in soil and rock masses can also be established, and users can upload crack pictures in real-time to share with other researchers, thereby establishing a huge soil and rock crack database, which is convenient for researchers to study and learn about surface cracks in soil and rock masses.
2. In addition, it is known from related work on Vision Transformer [31] that when the dataset is large enough, Vision Transformer performs better and achieves higher accuracy when trained on large-scale datasets. Therefore, Vision Transformer has strong potential in the application of large-scale datasets. In the future, more real slope surface cracks of soil and rock masses image data need to be collected to evaluate the performance of Vision Transformer in large-scale slope surface cracks of soil and rock masses datasets.
3. With the development of deep learning, there will be more deep neural networks with high accuracy and excellent performance in the future. This paper only conducts related research on the identification of cracks. In the future, more detailed research will be conducted on the location, length, width, and depth of the cracks. A deep learning model of slope crack detection suitable for UAV aerial surveys will be established to realize the practical application of deep learning in the field for the identification of slope cracks.
4. To achieve the expected objective of deploying deep learning models on UAVs, much preliminary research work was conducted in this paper to compare the recognition accuracy and computational efficiency of seven deep learning models and analyze them in conjunction with the total number of parameters and floating-point operations of the models. Among the seven deep learning models, the AlexNet model has the highest accuracy and computational efficiency. MobileNet, on the other hand, has the smallest computational volume among the models with the same magnitude of total parameters and thus has a relatively high computational efficiency. In practical applications, the deep learning models deployed on UAVs are generally the models that have been trained. The training process is generally performed before the model is deployed, so the number of training samples has a weak relationship with the final detection speed to enable the model to achieve optimal results. As for the selection of the deployed model, it is necessary to consider not only the accuracy but also the number of model parameters and computational size because this determines the memory occupation and computational efficiency of the model and try to select the model with a small memory occupation and high computational efficiency under the condition that the accuracy is guaranteed.
5. Currently, there are few deep learning models designed specifically for the identification of slope surface cracks. Therefore, future research work will focus on the design of related deep learning models for the identification of slope cracks. In addition, the deep learning model will be deployed on a UAV to monitor the high and steep slopes and other locations that are prone to geohazards and difficult to detect on-site cracks. Real-time monitoring of the surface cracks in soil and rock masses can be carried out to monitor geological disasters on high and steep slopes in a timely manner to ensure public safety and property.