## 5 Critical Analysis
### Segmentation  
[PERSON] et al. [48]. proposed the use of a commercially available sports camera, GoPro, to capture image data. They then constructed a novel method ConnCrack, which incorporates a conditional Wasserstein generative adversarial network (cWGAN) for road crack image detection. The method was pretrained on ImageNet and CFD datasets and compared with methods such as U-Net and VGG19-FCN on the homegrown dataset EdmCrack600, and the results show that ConnCrack has a significant advantage over other methods. [PERSON] et al. [26]. propose a new algorithm, FPHBN, to address the problem of low contrast between cracks and surrounding images. This algorithm is inspired by the HED framework and effectively detects road cracks. To verify the excellence of this method, the author compared it with four other methods, such as HED, RCF, etc. on five datasets, such as CRACK500, GAPs384, etc. Numerous experiments have shown that FPHBN outperforms the other methods in terms of accuracy. [PERSON] et al. [32]. collected road crack datasets using smartphones and digital cameras, but noise from the pavement background and roads affected the quality of these crack images. Conventional methods usually fail to extract accurate crack information from pavement images. This study proposes and optimizes CrackU-Net, a crack detection method using [PERSON]'s algorithm. The performance of CrackU-Net is compared with traditional methods, fully convolutional networks (FCNs), and U-Net, and the results show that CrackU-Net outperforms the other detection methods. [PERSON] et al. [82]. developed the densely connected deep neural network (DNN), an automatic road crack detection method that looks at how pixels are connected and adds a new loss function to fix the problem with the output of the transposed convolutional layer. The authors also compare the method to 11 other algorithms, including FPHBN, U-Net, CrackNet-V, etc., on two datasets, CFD and EdmCrack1000, and our experiments show that the method performs better overall. [PERSON] et al. [31]. proposed a DNN called CrackNet-V for pavement crack detection, which use the backbone with VGG, and a new activation function called leaky rectified tanh is proposed to improve the crack detection accuracy. Trained and tested on the PaveVision3 Ddataset, CrackNet-V has better overall performance compared to the CrackNet method, especially in the detection of small cracks. [PERSON] et al. [83]. proposed an efficient pavement crack segmentation model based on deep learning. The model is based on the pyramid attention network (PAN) and uses DenseNet121 as the encoder. Cross Entropy and Dice are the loss functions. To verify the effectiveness of the method, the author trained and tested it on Crack500, DeepCrack, GAPS384, and MCD datasets, and the experiments proved that the loss function can effectively improve the performance of the model. [PERSON] et al. [84]. created an adaptive feature fusion (AFF) module and a segmentation branch module for their proposed pixel-level crack detection network, APLCNet, to detect pavement cracks. The AFF module and the segmentation branch module were created to emphasize the location information of the cracks and to improve the detailed information of the mask prediction. On the CFD dataset, APLCNet achieves a precision of 92.21%, a recall of 94.89%, and an F1 score of 93.53%, demonstrating that the method outperforms CrackForest and MFCD. [PERSON] et al. [85]. propose a real-time pavement crack segmentation method inspired by Xception and BiSeNet. The model consists of two components: a spatial path and a contextual path. The spatial path uses three convolutional layers to encode sufficient spatial information. The Xception network, which rapidly down-samples the feature map, provides the basis for the spatial data. The feature map serves as the model for the contextual path network.