## 5 Critical Analysis
### Detection
In all aspects of the experimental results, the accuracy of YOLOv5 with the added attention mechanism outperforms that of YOLOv3, YOLOv4, and Faster CNN. The method is an effective means of enhancing the accuracy of YOLOv5. [PERSON] et al. [65]. proposed a novel annotation methodology, a dense and redundant annotation method, with the objective of enhancing the efficiency of data collection and reducing the time required for data collection in the process of road crack recognition. The method initially labels 800 images manually and then employs the trained initial model for training on a larger sample dataset. Following repeated correction and retraining of the misrecognized images, the total weight achieved a satisfactory experimental result. The method reduces the overall training time by 80%. This author tested 13 mainstream target detection methods and found that the method can still obtain good accuracy with a significant increase in training speed. [PERSON] et al. [66]. proposed a novel approach to enhance the road crack detection capability by combining the YOLOv5 architecture with the transformer module. The transformer's capacity to capture long-range dependencies enables the effective improvement of detection accuracy. The method was tested on an RDD dataset and compared with eight existing state-of-the-art methods, including Faster R-CNN, EfficientDet, YOLOv4, etc. It achieved high F1 scores, marking a significant advancement in the field of road maintenance technology. [PERSON] et al. [67]. proposed the context-augmented capsule feature pyramid network (CCapFPN), which is designed to efficiently and accurately detect road cracks under different conditions. Capsule networks are employed to encapsulate the intrinsic features of cracks, and a feature pyramid architecture is utilized to blend these features in order to enhance the resolution of cracks. A comprehensive comparison of seven algorithms, including CCapFPN and deep fully convolutional network (FCN), on eight datasets, such as PCD19, CFD, and Crack500, demonstrated that this method outperforms existing deep learning methods. [PERSON] et al. [68]. conducted a comparative analysis of the performance of Faster R-CNN, Mask R-CNN, and YOLOv3. The CRACK500 dataset was employed as the experimental dataset. The experimental results indicate that when the number of images exceeds 130, Faster R-CNN and Mask R-CNN exhibit superior performance compared to YOLOv3. In comparing the performance of Faster R-CNN and Mask R-CNN, it can be observed that Faster R-CNN typically provides more comprehensive bounding boxes. [PERSON] et al. [15]. proposed a semi-supervised approach for road crack detection using a mobile laser scanning (MLS) system. The method combines a graph convolutional network (GCN) with a graph-widening module, which serves to enhance the image. The method effectively reduces the amount of annotated data required, necessitating only a minimal amount to capture the local features of cracks. The method was trained and tested on a self-built dataset and demonstrated excellent recall and F1 score when compared with five other methods, including U-Net and AU-Net. This semi-supervised learning method reduces the reliance on large annotated datasets for road maintenance tasks. [PERSON] et al. [69]. proposed segmentation R-CNN, a model that combines pixel-wise and region-wise methods, effectively improves road crack detection. An additional branch for pixel-level segmentation was added using the improved Faster R-CNN framework, which was able to improve the detection of crack features. The performance evaluation was performed on a self-constructed dataset and compared with FCN and Mask R-CNN, and the results show that the method significantly reduces the computational cost and improves the accuracy of crack detection. [PERSON] et al. [70]. used Mask R-CNN and image processing techniques for road crack detection, which introduced a two-step process approach.