# Crack Location and Degree Detection Method Based on YOLOX Model
## 1 Introduction  
Automatic structural health monitoring (SHM) and maintenance have always been a challenging research field because of the aging of concrete structures (such as bridges, buildings, roads, and dams). The causes of concrete structural damage are various and complex, for example, earthquake, freeze injury, salt corrosion, and dry shrinkage [1]. As one of the earliest signs of structural damage, cracks on a concrete surface can be said to be the core detection part of the SHM [2; 3]. Therefore, the timely detection and assessment of concrete surface cracks can better grasp the state of a concrete structure to effectively prevent the occurrence of large-scale concrete structure accidents [4]. Currently, the traditional methods for crack inspection are still visual assessment techniques relying on inspectors. They are inefficient and expensive [2; 5; 6; 7]. Moreover, the assessment results are highly subjective [8; 9]. Thus, owing to the inevitable drawbacks of state-of-the-art methods, efficient and economical crack detection technology is urgently needed. In recent years, using image processing in crack detection is an appropriate way to address the above problems, and this technique is highly applied in the field of civil engineering [1; 2; 6; 10; 11]. Both [PERSON] et al. [10] and [PERSON] et al. [12] have reviewed the image processing methods for crack detection and evaluation of concrete structures. Generally, image processing technologies are practical and valuable. Although this research has made significant progress, there are still some shortcomings. They are too dependent on image characteristics and easily recognize noise (light, shadow, smudge, etc.) as cracks, which are not conducive to retrieving crack properties. Furthermore, most of the existing methods focus on identifying cracks in one single image. If no crack is recognized or the noise is incorrectly recognized as a crack, the detection results cannot be corrected in time. To tackle these issues, an automatic detection and evaluation system needs to be developed. Deep learning technologies have been widely used in crack detection of various structures, such as roads [13; 14], buildings [15], bridges [16; 17], tunnels [18; 19], and dams [20]. For instance, [PERSON] et al. [21] proposed an approach to classify each image patch in the obtained road crack images by training a supervised deep convolutional neural network(DCNN). However, this method ignores the complexity of image regions [22] and always overestimates crack width. [PERSON] et al. [23] adopted a sliding window to divide the image into blocks and used a convolutional neural network (CNN) to classify whether the block contains cracks or not. However, the computational cost of this approach is high because the CNN classifier needs to be applied multiple times to each window in every image. [PERSON] and [PERSON] [24] combined a CNN and Naive Bayes data fusion scheme to identify cracks in nuclear power plants through analysis of individual video frames. Nevertheless, this method only locates the crack position without quantifying the properties of cracks. In addition, when the amount of images is small, the system will not work well. [PERSON] et al. [25] applied a transfer learning (TL) technique to perform crack detection on the pavement. The VGG16 network was first pre-trained on an existing dataset and then automatically detected cracks from the FHWA/LTP dataset by using transfer learning. This is a successful attempt to apply TL in crack detection. [PERSON] et al. [26] presented an automatic pavement crack detection system based on CNN. This system, called CrackNet, predicted the category of all pixels to complete crack detection. However, the network architecture of CrackNet strictly depends on the input image size [27]. [PERSON] et al. [28] utilized Region CNN (R-CNN) based on a sliding window as a target detector to detect cracks in one concrete bridge. However, R-CNN is susceptible to the region and is inefficient compared with the current deep learning models.