## 5 Experiments and Results
### Comparison with Other Target Detection Models  
In the relative study of deep learning target-detection-model-based subway tunnel lining superficial defects and appurtenances, according to the model type division, the current mainstream two-stage target detection models Faster RCNN [23] and Cascade  
Figure 14: Comparison of the test results. The images shown from left to right are the input image, the weight assignment of the small target detection layer of the original model, the weight assignment of the small target detection layer of the proposed model, the weight assignment of the full detection layer of the original model, the weight assignment of the full detection layer of the proposed model, the detection result of the original model, and the detection result of the proposed model. (**a**) Facilities and crack detected result. (**b**) Facilities, crack and bolt hole detected result. (**c**) Facilities, crack, bolt hole, joint and exfoliation detected result. (**d**,**e**) Facilities, bolt hole and leakage detected result. RCNN [24] and single-stage detection models SSD [27], YOLOv3, and YOLOv5 were selected. Furthermore, the optimization of the model based on YOLOv5 was discussed in the earlier work of the research team by incorporating CA and BiFPN-feature fusion networks [51]. Therefore, comparison experiments were added to utilize the dataset of this study and impose the same optimization strategy by using YOLOv5s and YOLOv8s as the base frameworks. The models were defined as YOLOv5s-Improve and YOLOv8-Improve, which are jointly compared with the algorithms proposed in this study in terms of their comprehensive performance, which is judged by the mAP, the size of the model weight file, and the detection frame rate (ms/per image). The experimental results are shown in Table 4; the accuracies of the two-stage target detection models Faster RCNN and Cascade RCNN are greater than those of the SSD and YOLOv3 single-stage target detection models on the dataset. However, their model-weight files require a significant amount of memory, and model detection is inefficient. The detection speed and memory requirements of the YOLOv5 detection model are better than those of the other models. However, the detection accuracy of the apparent defect and other targets is slightly lower than that of the YOLOv8 model. Compared with the optimization strategies of similar studies, the detection model proposed in this study has greater accuracy in detecting apparent defects. The mAP is greater than that of both YOLOv5 and YOLOv8. Although the imposed optimization strategy adds additional computations which make the model detection speed and model memory occupation slightly lower than those of the smaller YOLOv5 and YOLOv8 base frameworks, the difference is minimal, and the model still achieves real-time detection and deployment of lightweight materials and has better performance in terms of the comprehensive performance of the model, which is more suitable for metro tunnel scenarios. The model has better comprehensive performance and is more suitable for detecting superficial defects in subway tunnel scenarios. Experimental Results of the Super-Resolution Enhancement of Apparent Cracks in Subway Tunnel Linings  
During the model pretraining phase, the DIV2K dataset was used to train the Real-ESRGAN, with the generator RRDB containing 23 blocks, a scale of 4, an initial learning rate of 0.0001, and [PERSON] as the optimizer, trained for 100K iterations. The generator training loss results of the pretrained model (Figure 15a) indicate that the generator model loss converges, demonstrating a good ability to generate high-resolution images, and can be used as a pretraining model for transfer learning. Subsequently, the generator and discriminator were trained on the created concrete crack image super-resolution dataset by combining the L1 loss, perceptual loss, and adversarial loss. The generator RRDB contained 23 blocks, and the discriminator network was U-Net.