# Transfer Learning Model for Crack Detection in Side SlopesBased on Crack-Net
## 2 Related Works
### Transfer Learning Method  
Transfer learning refers to a methodology that leverages pre-existing knowledge to address novel problems, which is particularly advantageous in practical scenarios characterized by limited data or challenging labeling processes [26]. [PERSON] et al. [27] introduced a unified transfer learning-based approach for detecting both pavement cracks and seal cracks, effectively addressing issues such as uneven intensity in pavement crack detection. [PERSON] et al. [28] developed an unsupervised representation federated transfer learning method, which significantly enhanced the accuracy of crack detection. Additionally, [PERSON] and [PERSON] [29] proposed a real-time concrete crack detection model utilizing a Transformer architecture, achieving superior precision in crack identification. [PERSON] et al. [30] presented a deep learning framework based on transfer learning to overcome the limitations of traditional crack detection methods for masonry building facades, which are often time-consuming and costly. [PERSON] et al. [31] introduced a crack detection technique grounded in the GoogLeNet Inception V3 convolutional neural network, resolving challenges associated with crack detection during the operational life cycle of civil structures. [PERSON] et al. [32] devised a deep transfer learning-based framework to tackle the difficulties of railway surface crack detection caused by complex image backgrounds and significant noise. Lastly, [PERSON] and [PERSON] [33] enhanced the EfficientNetB0 model through transfer learning, enabling efficient and objective crack detection on concrete surfaces while overcoming the inefficiencies and subjectivity inherent in traditional manual methods. [PERSON] and [PERSON] [34] proposed a pothole detection method for asphalt pavement based on transfer learning and convolutional neural networks, which solved the problems of low efficiency and strong subjectivity in traditional manual detection of asphalt pavement potholes. Despite the prevalence of transfer learning in numerous domains, research exploring its application in detecting cracks in high-steepness slopes remains limited. The scarcity of image data, particularly about cracks in such slopes, poses a significant challenge in training deep learning models. The irregular geometry of cracks further complicates accurately recognizing their minute characteristics. When feature extraction is performed in a U-shaped network, the number of feature channels in different layers does not match, resulting in insufficient fusion of deep and shallow features. To address these problems, this paper proposes a slope fissure detection method based on transfer learning. By capturing global and local detailed features and introducing an attention mechanism in feature fusion, the above problems are effectively addressed. It provides a reliable detection method for early warning of geological disasters such as landslides. A comprehensive review of the literature reveals that existing crack detection methods possess unique strengths across various technical paradigms, yet each approach is accompanied by specific challenges. These limitations are particularly pronounced when these methods are applied to the specialized context of slope crack detection. Table 1 provides a systematic overview of the main categories of crack detection methods and representative studies, as well as their respective advantages and and core limitations. It also offers a focused assessment of their applicability to slope crack detection. Deep learning-based transfer learning strategies have emerged as effective solutions to address the scarcity of annotated slope data. However, there remains a lack of research that specifically targets the unique characteristics of geotechnical slope cracks--such as their fine scale, irregular morphology, and susceptibility to strong background interference--while fully leveraging transfer learning to bridge domain gaps.