# A Multi-Scale Contextual Information Enhancement Network for Crack Segmentation
## 1 Introduction  
In recent years, as the urbanization rate of countries around the world increases, a large number of infrastructures, such as bridges, tunnels, and dams, are constructed, providing a solid guarantee for economic development and livelihood security. However, the supervision and maintenance of these facilities has also brought us new challenges. These infrastructures commonly use concrete as the construction material and the surface crack is one of the main symptoms of their damage and destruction [1; 2]. Without timely maintenance, cracks will have a significant impact on the service life and safety of those infrastructures. Other facilities, such as asphalt roads, also need to be checked regularly to ensure that surface cracks can be maintained and repaired in a timely way. Therefore, the automatic identification of surface cracks from optical images of various scenes is of great research importance [3]. Due to the development of computer science and image processing technology, it is now possible to partially automate the process of surface crack inspection. However, it is still a difficult task to accurately separate the cracks from the complex image background, as there may be dirt patches, oil stains, pitting, or other noise interferences. Most of the early crack segmentation techniques rely on traditional digital image processing methods, which often involve multiple pre-processing processes, such as morphological filtering [4; 5], fuzzy theory methods [6; 7], and wavelet transform [8; 9], as well as various crack segmentation methods, such as methods based on the thresholdalgorithm [10, 11] or the edge detection algorithm [12, 13]. Traditional digital image processing methods are sensitive to interference from external factors, such as light changes and shadow occlusion, making them unusable in complex scenes. Meanwhile, digital image processing methods require manually designed feature operators, which are more difficult and less efficient to implement. Recently, the application of deep-learning-based convolutional neural networks (CNNs) in the field of computer vision has developed rapidly and has even surpassed human performance in a variety of tasks, such as image classification [14, 15], object detection [16, 17], and semantic segmentation [18, 19]. Compared with traditional digital image processing methods, CNNs are characterized by their high level of automation and strong feature extraction capability, as CNNs do not rely on manually designed feature operators. In terms of crack recognition applications, some studies localize cracks in images by classification [20, 21] or object detection [22, 23] methods. However, these methods cannot obtain detailed information about the cracks, making them less optimal. Segmentation-based crack recognition methods annotate cracks in images at the pixel level, providing a better level of detailed information, as part of the current mainstream research direction [24]. Due to the special morphological characteristics of cracks, the crack segmentation task faces two challenges: the accurate segmentation of fine-grained spatial structures and the ability to adapt to complex background environments. The former requires that the multi-level feature information extracted by the feature extraction network can be fully utilized, while the latter requires the network to possess accurate context awareness. It is shown in [25] that feature maps in different levels explore distinctive information, with shallow feature maps possessing fine spatial information and deep feature maps capturing rich semantic information, while the conversion process from shallow to deep feature maps leads to a loss of detailed spatial information. To recover the lost spatial information in the decoder network, SegNet [26] assists the decoder in up-sampling by means of maximum pooling indexing, while U-Net [19] feeds the shallow feature information generated in the encoder directly to the decoder network by means of a skip connection.