## 5 Critical Analysis
### Detection  
[PERSON] et al. [42]. established and compared the performance of two intelligent methods for automatic road crack detection, showing that the model based on the CNN algorithm significantly outperformed the traditional edge detection approaches. The CNN algorithm constructed a model with a classification accuracy rate (CAR) of 92.08%. The CNN algorithm constructed a model with a CAR of 92.08%. [PERSON] et al. [12]. conducted a study to assess the efficacy of DCNN in the automated identification of road crack length in batches. To extract the properties of the cracks, k-means clustering analysis was employed to convert the original RGB images into grayscale maps. The DCNN network was optimized using stochastic gradient descent (SGD). Following the training and testing phases, the results demonstrated that the algorithm achieved a recognition accuracy of 94.36%, with a maximum length error of 1 cm and a mean squared error (MSE) of 0.2377. [PERSON] et al. [59]. used an IoT system with a bionic deep learning approach to improve the accuracy of road crack detection. The CRACK500 and GAPs384 datasets were employed to train the algorithm. Eventually, the algorithm's capabilities were confirmed, including the per-pixel accuracy (99.04%), Jaccard index (98.42%), loss error rate (0.03), precision (99.25%), recall (99.24%), and prediction accuracy (99.72%) metrics. [PERSON] et al. [60]. proposed to develop a method for road crack detection based on YOLOv3. The method is effective in recognizing longitudinal cracks, transverse cracks, alligator cracks, and potholes. The authors collected their own dataset for experimentation, and the results demonstrated a precision of 70% and an average IoU of 50.39%. The results demonstrate that the method exhibits high accuracy in the field of road crack recognition. [PERSON] et al. [61]. proposed a CNN for the purpose of learning crack features from images that do not require any preprocessing. They employed their own image dataset, collected from open-source sources, for training purposes. This dataset was based on the open-source TensorFlow framework developed by the Google Brain team. The experimental results indicate that the method exhibits satisfactory performance. [PERSON] et al. [33]. proposed a crack detection system based on deep learning models and 3D ground-penetrating radar (GPR). The method employs GPR to construct a dataset. The YOLOv4 and YOLOv5 models were employed for training and detection purposes on this dataset. Experimental results indicate that the YOLOv4 and YOLOv5 models exhibit a notable advantage over the YOLOv3 model in the context of training on a limited sample size dataset. The mAP of YOLOv5 is 94.39%. [PERSON] et al. [62]. developed a CNN framework that focuses on training image types that are difficult to detect in order to improve the training performance of the framework. The approach eschews the traditional method of increasing the number of training images and recursively improves this model's accuracy by collecting and analyzing images with recognition errors and retraining the CNN. The experimental results indicate that the accuracy of the model is up to 95%. [PERSON] et al. [63]. proposed a network model with supervised learning capabilities, which is able to detect cracks and lane markings. The model is based on the ResNet network and employs a feature pyramid network (FPN) for feature extraction. To train and test the network, the authors enhanced 1000 images to 20,000 images using image enhancement techniques. The experimental results demonstrate that the model exhibits a high degree of accuracy, with a rate of 96.5%. [PERSON] et al. [64]. selected YOLOv5 as the foundation for developing road crack detection methodologies. To enhance the accuracy of YOLOv5, the researchers incorporated additional attention modules, including SENet, ECANet, CBAM, and CoordAtt. A self-constructed dataset is employed in comparative experiments on different crack detection models.