# Transfer Learning Model for Crack Detection in Side SlopesBased on Crack-Net
## 1 Introduction  
In recent years, China's rapid economic development has led to a significant expansion of infrastructure construction, particularly in mountainous and hilly regions, including expressways, railways, and tunnels. Slope stability is a critical aspect of these projects, as it directly affects both project safety and the protection of lives and property. In complex geological environments, slope cracks are among the primary causes of slope instability [1]. Crack formation typically occurs gradually, making early detection challenging. Once cracks reach a critical size, they can trigger landslides, collapses, and other catastrophic events. Therefore, timely and accurate detection and assessment of slope cracks are essential for the prevention and control of geological disasters. Current slope crack detection methods include traditional manual inspection, the use of physical sensors, image processing techniques, and automated approaches based on machine learning [2]. Deep learning, particularly convolutional neural networks (CNNs), has demonstrated strong performance in crack detection tasks. Despite significant progress in slope crack detection, several challenges remain [3]. Recent studies in geotechnical and civil engineering haveshown that factors such as material type, surface moisture, inherent shadows, and vegetation coverage can significantly impact the detection and identification of slope cracks. These factors may obscure or alter crack features, increasing the complexity of automated detection [4,5]. For instance, variations in soil or rock composition and humidity can affect crack visibility, while shadows and vegetation may cause false positives or missed detections in image-based methods. These environmental and material factors have become key concerns in recent civil engineering research and are increasingly being considered in the development and application of information-based detection methods. First, slope cracks are typically slender and irregular, and their appearance is strongly influenced by environmental factors. This makes it difficult for traditional image processing and machine learning models to accurately identify small cracks, especially in complex backgrounds, where false detections and missed detections remain significant issues [6,7]. Secondly, most deep learning models require large amounts of labeled data for training. However, in slope crack detection, data are limited, and labeling is challenging, which hinders model training and generalization [8,9]. Additionally, the robustness and real-time performance of current models require further improvement, particularly when dealing with complex geological environments and diverse crack morphologies. Detection accuracy often falls short of engineering requirements [10]. To address these challenges, this paper proposes a crack detection model based on transfer learning. The model is initially trained on a large-scale fissure dataset; then, transfer learning is applied to adapt the model to specific slope crack datasets. This approach effectively mitigates the issue of limited data and enhances the model's generalization ability and accuracy. To address the challenges of complex crack morphology and the detection of fine cracks, a Frequency-Domain Nonlinear Mapping (FDNM) module is introduced. This module enhances frequency-domain features of crack images, enabling better capture of fine crack characteristics and improving detection accuracy. Additionally, to optimize model performance in complex backgrounds, a Bi-Attention Fusion Module (BAFM) is designed. This module fuses multi-level features, activates crack-related features, and suppresses non-crack features, thereby enhancing the model's ability to recognize crack details. The main contributions of this work are outlined as follows:  
1. We propose a frequency-domain nonlinear mapping module to better capture the characteristics of small cracks using fast Fourier transform and enhancing the amplitude and phase information;
2.