## 5 Critical Analysis
### Detection
The Mask R-CNN was first trained on a self-generated dataset, which was able to classify the cracks more accurately with an accuracy of 92.10%. The width of the cracks was then measured to assess the severity of the cracks. This approach significantly improves the efficiency of the road management system. [PERSON] et al. [71]. proposed a two-stage deep learning method, DDSNet, for road crack detection. The first stage is to optimize the YOLOv4 algorithm to identify cracks in complex environments by providing the specific locations of the cracks. In the second stage, the Deeplabv3+ model is used to segment the detected cracks at the pixel level. Comparison with nine existing models, such as SegNet, Unet, PSPNet, etc., provides significant advantages in inference speed and accuracy. Furthermore, the authors provide a scalable solution that adapts to different training data volumes and conditions. [PERSON] et al. [72]. proposed a closed-loop feedback (CLF) approach using generative adversarial networks (GANs) for road crack detection, called CrackLF. This method is capable of more accurately identifying cracks and the background. The effectiveness of CrackCLF was verified by testing it on three datasets: CFD, Crack500, and Crack700. Compared with methods like U-Net, DeepCrackZou, U-HDN, this method shows excellent performance in precision and recall. [PERSON] et al. [73]. proposed a deep learning model called SegDecNet++. The method combines pixel segmentation and image classification and uses the classification results to improve segmentation accuracy in the training and inference phases. The method trains on a combination of CFD, CRACK500, CrackTree200, DeepCrack, GAPs384, Rissbilder, and non-crack datasets, and achieves a good Dice score of 81% and an IoU of 71%. Ablation experiments showed that the integration of classification information significantly improves the segmentation performance. [PERSON] et al. [74]. proposed the CrackNet-M method to achieve 3D image-based road crack detection. It incorporates the central branch net (CBN), crack map enhancement (CME), and pooling feature pyramids modules to improve the crack detection capability and detection speed of the method. The model, trained on a self-generated dataset of 2500 3D images and then tested on a test dataset of 200 images, achieves high precision (94.28%), recall (93.89%), and F-measure (94.04%). The model can effectively detect coarse and fine cracks, as well as complex road conditions such as shoulder collapse. [PERSON] et al. [75]. developed a two-step shadow removal-oriented crack detection (SROCD) method that focuses on removing shadows before finding cracks. This makes the method more stable under different lighting conditions and eliminates the problem of shadows interfering with road crack detection. To better account for shadow interference, a targeted dataset called Shadow-Crack was developed that contains different types of shadows and cracks. After training on this data and testing on the GAPs 384, Cracktree 200, Crack500, CFD, and AEL datasets, it is compared to the FCN, residual complex filter (RCF), holistically nested edge detection (HED), and feature pyramid and hierarchical boosting network (FPHBN) methods. The results show that shadow removal-oriented crack detection SROCD has excellent performance in shadow removal and crack detection. [PERSON] et al. [76]. present a robotic system based on the depth-based RCDNet for road crack detection. The robotic system, which combines navigation and road crack detection, can be used in both indoor and outdoor environments. An encoder-decoder architecture and advanced modules such as context-embedded channel attention (CECA) and global attention module (GAM) are used by the RCDNet to make crack detection more accurate. Tested on the Crack500 dataset, the accuracy is 96.29%, the cube coefficient is 97.33%, and the IoU is 96.90%. Based on the detected cracks, the system can also create a crack severity map that indicates the areas that need road repair. [PERSON] et al. [77].