# A Rapid Bridge Crack Detection Method Based on Deep Learning
## 1 Introduction  
Concrete is widely used in dams, bridges, and other large-scale engineering structures [1]. For these structures, maintenance, monitoring, and life assessments are very important tasks after the post-construction period [2; 3; 4]. Among the various disasters that can occur during the maintenance period of bridge engineering, cracks often appear first [5]. This is due to the uneven settlement of the bridge foundation in the vertical direction and displacement in the horizontal direction, leading to internal stresses in the concrete structure and resulting in cracks [6]. For foundations that are built in phases or subjected to the effects of frost in cold areas, deformation of the structure and cracks can also occur [7; 8]. Additionally, bridge cracks can cause significant harm: (1) Cracks can result in leaks, causing water flow to into the interior of the bridge and destroying the concrete's mechanical characteristics and physical properties. When flowing water freezes, it will cause the formation of deeper and wider cracks, which will cause instability of the main structure of the bridge, decreasing the safety level. The water in the bridge cracks will cause further development and expansion of cracks under the influence of gravity and pressure during construction [9]. (2) The cracks will lead to carbonation in the concrete structure of the bridge. The concrete material reacts with CO\({}_{2}\) from the environment in the presence of moisture to produce calcium carbonate. As a result, the safety and mechanical properties of the concrete structure are reduced [10]. (3) Bridge cracks will destroy the purification filmof steel bars and metal components and corrode under the simultaneous penetration and action of external air and water. The rust generated after the corrosion of steel bars is more than ten times larger than the initial volume, which will reduce the stability of the reinforced concrete engineering [11]. In summary, it is very important to obtain the location, length, width, and extension condition of bridge cracks in time through detection technology. However, with continuous increases in the number of completed bridges and bridge spans, crack detection work becomes increasingly demanding. At the same time, the economic costs also become higher [12]. With the accelerated growth of computing capabilities in recent years, advancements in deep learning technology have motivated us to develop a novel approach to address these problems [13; 14; 15]. [PERSON] et al. [16] presented a novel approach for bridge crack detection by enhancing the encoder-decoder framework and utilizing a mixed pooling module. The encoder employs dilated convolutions to extract the fundamental characteristics of crack images. This methodology ensures preservation of the feature image resolution and facilitates the acquisition of a wide receptive field. Notably, the experimental results demonstrated that this technique achieved significantly higher precision and recall metrics. [PERSON] et al. [17] proposed the utilization of deep learning techniques for bridge crack detection using unmanned aerial vehicles (UAVs). They adopted the faster region convolutional neural network (faster R-CNN) algorithm based on VGG16 transfer learning to detect bridge cracks effectively. The experimental results indicated that the automatic detection of bridge cracks using UAVs and the faster R-CNN algorithm could provide enhanced efficiency without compromising accuracy. By leveraging the advantages of depthwise separable convolution, atrous convolution, and the atrous spatial pyramid pooling module, [PERSON] et al. [18] presented a convolutional neural network (CNN) end-to-end crack detection method. This study showed that the model had better capabilities compared with conventional classification models. Moreover, the model had the potential to be integrated into any convolutional network, serving as an efficient module for feature extraction.