## 5 Critical Analysis
### Classification
developed a road crack detection method using 3D ground-penetrating radar with deep learning techniques. The method uses two models: CP-YOLOX for localizing anomalous waveforms and scattering vision transformer (SviT) for classifying these waveforms into distress categories. The authors compared the self-generated method with methods such as MobileNet and ResNet50 using their own collected dataset, and the results indicated that this method performed the best. The authors also found that a larger number of datasets can improve the accuracy and reliability of the method. [PERSON] et al. [11]. proposed a crack detection method based on images with small samples. [PERSON] et al. preprocess the limited sample images in two steps to solve the problem of insufficient sample size and the complexity of image content. First, [PERSON] et al. use the data enhancement technique to significantly expand the dataset, increase the number of images, and solve the data imbalance problem. Then, the original images are converted into binary black-and-white images to effectively reduce the complexity of image features. Next, the authors test the method in the AlexNet, SE-Net, and ResNet frameworks, and the experiments demonstrate its effective improvement of crack classification accuracy. [PERSON] et al. [53]. introduced a CNN for detecting small objects such as cracks. The method incorporates multiple feature layers to improve the detection capability. The method uses multi-scale feature extraction and anchor boxes with different aspect ratios to improve the speed and accuracy of crack detection. The method achieves an impressive speed of 96.6 frames per second with an accuracy of 98.217%, which is far better than the YOLOv1, YOLOv2, and fast region-based convolutional neural network (Fast R-CNN) algorithms. [PERSON] et al. [54]. proposed an FS-Net-based crack detection method for roads. To better capture crack features, the method uses flexible rectified linear units (FReLU) instead of the traditional leaky ReLU. To improve crack detection, maximum pooling is replaced by strip pooling. The authors perform training and testing on a self-generated dataset and compare it to R-CNN and YOLOv3. The test results show that the method has a faster processing speed than R-CNN and YOLOv3, but still has a high detection accuracy. [PERSON] et al. [55]. proposed a faster region-based convolutional neural network (Faster R-CNN) for road crack detection. The model uses a regional proposal network (RPN) for crack detection, which is able to quickly detect targets of different sizes in a multi-target scenario. Different types of cracks are detected on a self-constructed dataset, and the results show that the method is able to detect and classify different cracks at a high level, effectively reducing the dependence on manual detection. [PERSON] et al. [56]. conducted an investigation into the integration of the vision transformer (ViT) module into the YOLOv5 model to enhance the accuracy of road crack detection. The method achieved a mean average precision (mAP) of 87.2% on a self-constructed dataset of 1944 images, and the detection process took only 11.9 ms per image. The test results demonstrate the effective application of the method in real-time road crack monitoring. [PERSON] et al. [57]. used an unmanned aerial vehicle (UAV) to acquire a dataset, which they then combined with the YOLOv2 model to detect the dimensions of cracks in an airport runway and pinpoint their locations on the concrete surface. The YOLOv2 detector successfully identified cracks with high average precision (AP = 0.89), demonstrating its potential for real-world use. The system can effectively replace manual inspection, making the safety of airport runways safer and more efficient. [PERSON] et al. [58]. suggest using architectures such as VGG16, ResNet-34, and ResNet-50 to explore multi-label classification (MLC) and convolutional neural networks (CNNs) to find cracks in the road. Tested on a self-generated dataset, the method achieves up to 97% accuracy and a 93% F1 score.