## 5 Critical Analysis
### Segmentation
The model is trained and tested on the Crack500 dataset, and the experimental results show that it is comparable to four other methods, such as FC-DenseNet103, DenseASPP, and DeepLabV3+. In comparative tests, the method achieves superior performance in terms of frames per second (FPS). [PERSON] et al. [86]. proposed an encoder-decoder network (EDNet) that modifies the ResNet-34 architecture as an encoder network, drawing inspiration from CNN-based autoencoders. In this paper, the authors experimented using the PaveVison3D and CFD datasets and compared this method with models such as CrackForest, Cracknet-V, and U-Net, and the results showed that EDNet outperformed the others. The F1 scores on PaveVison3D and CFD were 97.80% and 97.82%, respectively. [PERSON] et al. [87]. proposed a 3D crack segmentation model that combines the multi-view stereo imaging technique with U-Net to achieve engaged crack and pit segmentation. To complete the experiment, the team's proposed approach uses a 3D pavement model to simultaneously generate color, depth, and overlapping images. Experiments on this dataset show that the model outperforms GCU-Net models in terms of segmentation accuracy and inference speed. [PERSON] et al. [88]. proposed a fully convolutional neural network based on deep supervised networks and dense connectivity for image pixel-level detection. The deep supervision module can effectively improve the performance of the network, the network can extract more features, and the dense connectivity layer can effectively highlight crack features. Finally, we fuse feature maps of different scales to realize the complementarity of different levels. To verify the effectiveness of the different modules in the model, this author designed ablation experiments. The final experiment demonstrates the high accuracy, speed, and robustness of the method. [PERSON] et al. [89]. The goal of the research was to develop a road crack detection method with high real-time performance. Based on the U-Net network architecture, the DAUNet detection method was developed. To validate the effectiveness of the method, experiments and tests were conducted on five datasets, such as Crack500, GAPs384, and CrackTree200. And it is compared with four mature methods, such as FPHBN. The experimental results show that the method has high superiority in crack detection in complex environments. [PERSON] et al. [90]. proposed to eliminate the noise of the road image based on a fractional integral-based filtering method while preserving the persistent texture information of the road image, and to use the fractal dimension to detect the shape of the cracks. A network called fractional integral and fractal dimension (FIFD) is designed. Extensive experiments are conducted based on a self-constructed dataset. The experiment compares the threshold, edge, valley and region methods, and the experimental results show that the method outperforms the existing methods in terms of accuracy and generality. [PERSON] et al. [91]. developed a weakly supervised learning approach for road crack detection to make road crack detection more cost-effective. The method proposes to compute pseudo-labels using the GrabCut algorithm and proposes a dynamically balanced binary cross-entropy loss function, which is used to solve the imbalance of positive and negative samples. The results show that the method is able to detect road cracks with a high detection rate despite the reduction of manual labeling. [PERSON] et al. [92]. introduced a new deep learning framework for road crack detection. This framework, called W-Segnet, has two encoder-decoder models with multi-scale feature fusion and skip connections. The model was trained and tested on four datasets--CFD, CRACK500, CrackTree200, and EdmCrack600--and the results showed that the model performs very well in different scenarios and outperforms existing models such as U-Net, SegNet, and PSPNet. [PERSON] et al. [10].