## 5 Critical Analysis
### Segmentation
proposed the idea that color is not a critical feature for crack detection in deep learning models for image-based road crack detection. To investigate the effect of color on CNN performance, the authors processed 40,000 RGB images using grayscale, thresholding, and edge detection image processing techniques. The experimental results show that the performance of grayscale images is comparable to that of RGB images, indicating that color is not a critical feature for the deep learning model. Thresholding and edge detection show performance degradation. This study highlights that grayscale preprocessing improves the efficiency of crack detection without losing key features. [PERSON] et al. [93]. proposed a novel deep learning framework called PCDNet that aims to effectively detect pavement cracks using 3D images. PCDNet combines a CNN with an improved pixel-level crack seed algorithm in a three-step process that divides 3D pavement images into patches, finds cracks, and ensures that they remain connected. The author created a dataset of 4300 images to train and test the model. Comparisons with the improved canny and crack seed methods show that PCDNet has high precision, recall, and F1 scores, while this method significantly reduces the time required to train the dataset. [PERSON] et al. [94]. proposed the DCANet-SE-ResNet method for road crack detection. The model is based on ResNet50 and includes hierarchical feature fusion and an associated attention mechanism. To refine the crack details, the depth separable convolution is combined with the dilated convolution. This approach improves the performance of crack edge representation and the overall segmentation capability. Performance is compared with FPHBN, DeepCrack, U-Net, SegNet, and CrackSegNet on the CFD, Crack500, and DCD datasets. The overall performance is superior to these existing methods. The algorithm has made significant progress in its ability to distinguish cracks from spots and obstructions. [PERSON] et al. [95]. proposed the enhanced high-resolution semantic network (EHRS-Net) to optimize pixel-level crack detection in pavement evaluation. To enhance image detail, the method combines resolution maintain flow (RMF) and stacked atrous spatial pyramid pooling (SASPP). Performance evaluations on the CFD-ex and HRSD datasets have shown excellent results. In particular, the ability to detect microcracks is leading. [PERSON] et al. [96]. proposed to use the feature fusion module in the encoder-decoder architecture to extract detailed crack features, enabling the creation of a global context and pyramidal scale-guided convolutional neural network, primarily for small-target detection. Cross-entropy, Tversky, and Lovasz hinge losses resolve the imbalance between cracks and background pixels. Training and testing on four datasets, Crack500, DeepCrack, GAPs384, and MCD, show that the method is not only highly accurate but also fast, providing an excellent solution for road crack detection. [PERSON] et al. [97]. present a semi-supervised semantic word segmentation method using cross-consistency training (CCT) for efficient road crack detection. This method relies on unlabeled crack samples to ensure consistency between primary and secondary decoderpredictions. These predictions examine modified versions of the same encoder output. The method labels only 60% of the data, reducing manual labeling effort. When tested on a self-generated dataset, it outperforms methods such as U-Net, SegNet, and Deeplabv3+. [PERSON] et al. [98]. introduced an efficient crack segmentation neural network (ECSNet) for real-time road crack detection. The network integrates small kernel convolutional layers, parallel max-pooling, and convolutional operations, which can effectively reduce model parameters and improve computational efficiency. Tests are performed on the DeepCrack dataset, and performance comparisons are made with DeepLabV3, FCN, LRASPP, Enet, Unet, and DeepCrack. The results show that DeepCrack completes training in the shortest time and achieves the second highest accuracy.