# Crack Location and Degree Detection Method Based on YOLOX Model
## 4 Experimental Results and Analysis
### Test Results and Discussion  
To verify the superiority of using the YOLOX model as a crack location and degree detector, this study comparatively analyzed the recognition performance of 10 models with 4 different deep learning networks, i.e., YOLOX (YOLOXs, YOLOXm, YOLOXl, and YOLOXx), Faster R-CNN, DSSD, and YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x). To ensure fairness, this study is calculated on the same dataset and training platform, and each model has been effectively trained, and their parameters are selected with the best choice. Table 4 lists all testing results of different deep learning models, including mAP value to measure the detection effect, FPS and inference time to measure the real-time performance and the model training time to measure the complexity. In general, the mAP values of YOLOX series models all exceed 88.5%, especially the YOLOXx. The best mAP value is 91.05% in YOLOXx. Even in YOLOXs the mAP value is 88.71%. These results demonstrate the applicability of YOLOX series models in the detection of cracks with different degrees. In contrast, the mAP value of the Faster R-CNN is only 69.77%, the mAP value of the YOLOv5 is the highest at 88.11%, and the mAP value of the DSSD is slightly higher at 88.92%, but other performance indicators are bad. Therefore, the YOLOX model has significantly higher accuracy in identifying the location and extent of cracks. Figure 10: Validation results of the YOLOX model at different learning rates. Furthermore, object detection needs to ensure better real-time performance. The FPS and inference time are also shown in Table 4. With the exception of the FPS of Faster R-CNN being only 22.69, the other models all exceed 30, which can complete the real-time detection. The FPS of YOLOv5_s is up to 500, and the inference time is also the fastest. It takes just 2 ms to detect the cracks with extent in an image. Of course, the inference times of YOLOX are also rapid, all with 15 ms, and the inference time of the YOLOXs is only 1.51 ms longer than that of the YOLOv5s. Additionally, Table 4 shows the training time of each model; the longer the training time, the more complex the model network structure. The training time of the YOLOXs for the training set in this paper is 0.583 h, which is only 0.24 h more than the training time of the YOLOv5s, whereas the training of the DSSD is the most time-consuming, requiring 4.822 h.  
In this study, the crack degrees are divided into five categories according to the different crack widths. Table 5 presents the detection test results of each crack degree, which is expressed by AP value. The YOLOX has superior detection results for wide crack, normal crack, thin crack, and spalling, which are 92.59% (YOLOXI), 91.65% (YOLOXx), 84.75% (YOLOXx), and 99.86% (YOLOXs), respectively. The best AP value of the super wide crack is 95.36% from YOLOv5x. Generally, super wide crack and spalling are easy to detect due to their severe damage and prominent features. In contrast, thin crack, with a width of less than 1 mm, is more difficult to detect because of the small degree. In this study, the crack location and degree detector constructed by the YOLOX model can realize the identification of thin crack with a high accuracy, which is a great breakthrough. The AP value of YOLOX for thin crack is above 74%, which highlights the advantages of the crack location and degree detector for the detection of small features. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **mAP (\%)** & **FPS** & **Inference Time (ms)** & **Training Time (h)** \\ \hline YOLOXs & 88.78 & 284.90 & 3.51 & 0.583 \\ YOLOXm & 89.98 & 159.74 & 6.26 & 0.900 \\ YOLOXI & 90.17 & 112.36 & 8.90 & 1.517 \\ YOLOXX & 91.05 & 68.49 & 14.60 & 2.533 \\ \hline Faster R-CNN & 69.77 & 22.69 & 44.07 & 1.867 \\ DSSD & 88.92 & 97.47 & 10.26 & 4.822 \\ YOLOv5s & 85.85 & 500.00 & 2.00 & 0.343 \\ YOLOv5m & 86.33 & 212.77 & 4.70 & 0.598 \\ YOLOv5l & 87.85 & 121.95 & 8.20 & 0.889 \\ YOLOv5x & 85.11 & 65.79 & 15.20 & 1.811 \\ \hline \hline \end{tabular}
\end{table}