## 5 Experiments and Results
### Comparison with Other Target Detection Models
During training, the batch size was 16, and the initial learning rates for the generator and discriminator were both 0.0001, with [PERSON] as the optimizer, trained for 150K iterations. The training loss curves of the generator and discriminator are shown in Figure 15b,c. The generator and discriminator reached a balance during continuous adversarial training, effectively performing image super \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **mAP@50(Defects)** & **mAP@50(All)** & **File Size/MB** & **ms/per Image** \\ \hline Faster RCNN-res50 & 0.739 & 0.78 & 327.31 & 29.1 \\ Cascade RCNN-res50 & 0.754 & 0.805 & 543.4 & 38.9 \\ SSD & 0.72 & 0.754 & 190.36 & 15.7 \\ Yolov3-d53 & 0.723 & 0.79 & 476.23 & 18.4 \\ Yolov5s & 0.74 & 0.811 & 13.95 & 6.8 \\ Yolov5s improve & 0.763 & 0.825 & 14.33 & 8.5 \\ Yolov8s & 0.768 & 0.827 & 22.04 & 8 \\ Yolov8s improve & 0.798 & 0.848 & 22.25 & 9.2 \\ Proposed model & 0.838 & 0.87 & 23.37 & 10.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison results with other models. resolution reconstruction. The losses of both the generator and discriminator fluctuated within a reasonable range, indicating good training results. When the proposed metro tunnel defect detection model was applied, crack images could be obtained based on the relative coordinates of the detected fine-crack areas (Figure 16). These images were used as test data, and the trained Real-ESRGAN model was utilized to perform super-resolution restoration on the acquired low-resolution crack images. Additionally, traditional bicubic interpolation and deep learning-based super-resolution models, such as the SRGAN and ESRGAN, were used for the experiments in this study. The relevant models used the official pretrained models and parameters from the DIV2K dataset during training. The comparative test results are shown in Figure 17.  
From a visual subjective perspective, traditional interpolation methods have significant limitations in enhancing details and removing blur from crack images. In contrast, deep learning methods can enhance image clarity to varying degrees by learning the morphology and texture features of concrete cracks. The SRGAN provides some texture enhancement and detail restoration for the main crack areas but is limited in enhancing branch areas. Figure 16: Extraction of apparent crack areas in subway tunnel lining using the proposed detection model. Figure 15: Training results of Real-ESRGAN based on the transfer learning method. (**a**) The first training loss of the generator by using DIV2K dataset, (**b**) the second training loss of the generator by using concrete crack image super-resolution dataset, (**c**) the second training loss of the discriminator by using concrete crack image super-resolution dataset. The ESRGAN shows better restoration capabilities for both the main and branch structures of cracks but still shows noticeable artifacts in the background area and lacks performance for some potential detailed information. The application of Real-ESRGAN results in strong detail enhancement for crack areas and better representation of the concrete background, achieving good results in local detail enhancement and artifact suppression. Given that the experimental data did not include high-resolution crack region images as true labels, no-reference image quality evaluation metrics, a perception-based image quality evaluator (PIQE) [52], and an entropy-based no-reference image quality assessment (ENIQA) [53] were used for objective quantitative evaluation. The PIQE calculates the no-reference quality score of the image via block-wise distortion estimation. ENIQA extracts mutual information and 2D entropy from the spatial and frequency domains of an image and uses a support vector classifier and support vector regression to predict the image quality score. The lower the PIQE and ENIQA values, the greater the image quality.