# Deep Transfer Learning Approach for Identifying Slope Surface Cracks
## 1 Introduction  
Geological disasters (geohazards) frequently occur worldwide, and various geological disasters have caused great harm to public safety and property [1, 2, 3]. The occurrence of geological disasters, such as landslides, debris flows, rock collapses, surface subsidence, surface collapse, surface cracks, and earthquakes, is often accompanied by the occurrence of surface cracks in soil and rock masses. Especially for high and steep slopes, which are more prone to geological disasters, such as rock collapses and landslides (as illustrated in Figure 1), a series of cracks will form during the early stage of geological disasters. The cracks caused by a collapse are illustrated in Figure 2 (source: www.news.sohu.com and www.heraldnet.com, accessed on 15 April 2021). Before a rock collapse occurs, there are cracks on the top of the slope; the cracks at the rock collapse area will gradually expand, and new cracks will appear at the foot of the slope over time [1, 2]. Therefore, monitoring and identifying cracks on the top of a rock collapse slope can effectively provide an early warning of collapse and reduce the loss of human life and property [2, 3]. Landslide cracks are illustrated in Figure 2. Landslide cracks are an important sign accompanying landslides and can be divided into tensile cracks, shear cracks, bulging cracks, and fan-shaped cracks [1, 2]. In the early stage of landslide formation, intermittent cracks will appear on the rear edge of the landslide [4, 5], and the length of the cracks tends to be constant. Continuous cracks appearing on the trailing edge and the length of the cracks showing an expanding trend indicate that the landslide is slowly intensifying [6, 7]. As the landslide slides down, a shear zone will be formed between the landslide and the parent body, and shear cracks will appear. The soil and rock masses distributed atthe front of the landslide body will also form open cracks due to the uplift of the soil and rock masses hindered by the slide of the landslide body. At the same time, the spread of the landslide tongue to both sides will also form fan-shaped cracks [8; 9]. The change in landslide cracks reflects the development process and extent of the landslide. Therefore, the identification of cracks at different positions of the landslide is of great significance for the early monitoring and warning of landslides [10; 11]. Currently, the most commonly used crack detection method is manual detection. However, there are several problems in manual detection, including detecting blind spots in the detection process, high fieldwork intensity, low work efficiency, the subjective impact of detection results, and low accuracy [12; 13]. To address the problems arising from manual detection, various methods based on image identification have been developed [12; 14; 15]. However, methods based on image identification are easily affected by environmental factors, such as light, shadows, background, etc., and the computing of later image-based identification methods is often very complicated, resulting in low computational efficiency. The reliability of the detection result of a crack picture with a complex background is unsatisfactory [16]. In recent years, deep learning has achieved good performance in object identification tasks and has the advantages of high parallelism, good robustness, and strong generalization ability [17]. Deep learning does not need to design artificial feature extraction, especially in image classification; the accuracy of trained deep learning network models is significantly higher than that of traditional machine learning approaches [18; 19; 20]. Many deep learning models have been produced with higher accuracy, generalization ability, and robustness. Applying them to crack detection can effectively improve the efficiency of crack identification and make crack detection more efficient [21; 22].