# Crack Location and Degree Detection Method Based on YOLOX Model
## 1 Introduction
[PERSON] et al. [29] compared the DCNN and some traditional edge detectors for image-based concrete structural crack detection. It concluded that the AlexNet DCNN is better at crack detection. Yet the superior model may be further improved when adopting some new networks. [PERSON] et al. [30] offered a CNN-based crack detection technique by combining vision and infrared thermography data which can minimize the false detection rate. [PERSON] et al. [31] introduced a deep-width network (DWN) architecture to classify cracks without handcraft feature extraction. Although this method avoids hyperparameter tuning to improve efficiency and accuracy, the algorithm is complicated, which limits its development and extensive application. [PERSON] et al. [32] detected concealed cracks from images using deep learning models. This research compared and discussed some object detection models. However, it does not apply a high-performance GPU, which affects the detection efficiency. In summary, the above research proves that deep learning is excellent in crack identification. However, detecting structural cracks is only the beginning of SHM, as researchers and inspectors often pay more attention to the state of detected cracks. It is not enough to just locate the crack but not identify the damage degrees of the crack. Thus, it is essential to design an application that can directly obtain crack condition information and crack position. With the fast growth of deep learning methods, many excellent deep learning technologies have been proposed. Concrete structural crack detection includes target detection, which can identify and locate cracks. Object detection techniques based on deep learning could be classified into two types: two-stage approaches and one-stage approaches. Two-stage approaches, for example, R-CNN [33], Fast R-CNN [34], and Faster R-CNN [35], first use the image information to decide the potential position of the target, and then CNNs is used to classify and retrieve the characteristics from these positions. Although this method provides high accuracy, the detection speed is slow. In contrast, one-stage methods use end-to-end CNN to estimate the bounding box position and type of objects in multiple positions. The detection speed can be greatly accelerated because of the simplified detection process. The main representatives of one-stage approaches are You Only Look Once (YOLO) series algorithms [36; 37; 38; 39; 40; 41], Single Shot Multibox Detector (SSD) [42], and Deconvolutional Single Shot Detector (DSSD) [43]. The YOLO series as a popular one-stage detection method is applied extensively. YOLOv1 algorithm was originally proposed by [PERSON] et al. [36] in 2016. It forms an end-to-end CNN by integrating each independent step in the process of object recognition. Object detection is regarded as a regression process, and the object location and category of the detection image can be obtained by processing the input image. The detection speed of YOLOv1 is fast, but the detection accuracy is low. Subsequently, a series of YOLO algorithms are proposed (YOLOv2 [37], YOLOv3 [38], and YOLOv4 [39]) with the YOLO methods developing and improving rapidly. They gradually balance the detection efficiency and the precision detection. In addition, the detection results of small targets grow better. Recently, two novel YOLO models were updated, that is YOLOv5 [40] and YOLOX [41]. These two versions combine some progressive methods, which have more advantages and more efficiency in real-time image processing. At present, there are few studies on damage recognition using the YOLOv5 or the YOLOX. Consequently, the application effect of newer YOLO series models in concrete damage remains to be further investigated. In this study, an algorithm for detecting and evaluating different degrees of cracks in images is proposed. The defects in the image are labeled according to the width of cracks, which compose a new image dataset with different degrees of crack.