## 3 Deep Learning Algorithms
### YOLO Series Algorithms  
The YOLO series algorithm is a typical representative example of the one-stage algorithm target detection model. In comparison to the Faster RCNN algorithm, YOLO eliminates the need to extract candidate regions that may contain targets. It completes the detection task using only one network and predicts the category and location of the target object in the detection output through regression. Currently, YOLOv5 is the initial model of the series, which has been proven to be stable and is widely used in lightweight road crack detection methods due to its excellent accuracy [17; 21]. YOLOv5 consists of several networks with different depths, namely n, s, m, l, and x. The depth and width of the network increase in the order of n, s, m, l, and x. Among these options, YOLOv5s is suitable for small deep networks or small-scale datasets. The network architecture of YOLOv5 is depicted in Figure 4. The model comprises three main components: the backbone network (BackBone), the neck network (Neck), and the head detection network (Head). The backbone network (Backbone) primarily performs feature extraction by utilizing a convolutional network to extract object information from the image. This information is then used to create a feature pyramid, which is later employed for target detection. The backbone network consists of various modules, such as the Focus module, Conv module, C3 module, and SPFF module. Notably, the SPPF (Spatial Pyramid Pooling Faster) module is capable of converting feature maps of any size into fixed-size feature vectors. This allows for the fusion of local and global features at the Feather Map level and further expands the receptive field of the feature map. Consequently, objects can be effectively detected even when input at different scales. The neck network (Neck) is responsible for the multi-scale feature fusion of the feature map. It adopts the structure of the Feature Pyramid Network (FPN) and the Path Aggregation Network (PAN), which enhances the model's ability to capture object features at various scales and improves the accuracy and performance of target detection. The head network (Head), also known as the detection module, utilizes techniques like anchor boxes to process the input feature mapping and generate regression predictions. These predictions include information about the type, location, and confidence of the crack detection object. YOLOv7 [27] is an enhanced target detection framework based on YOLOv5. It incorporates a deeper network structure and robust methods, resulting in an improved accuracy and speed compared to YOLOv5. YOLOv7 introduces several techniques, such as Long-Range Attention Off Network (ELAN) and Bottleneck Attention Module (BAM), to enhance its learning capability. ELAN expands, shuffles, and merges the quantity (Cardinality), thereby improving the equilibrium state of the learning network. To prevent overfitting, YOLOv7 employs a regularization method similar to DropBlock. This regularization method enhances the stability and robustness of the model, enabling it to be trained on larger datasets. Figure 3: An illustration of Faster-RCNN (modified from [29]). YOLOv5 [28] was released in January 2023 by Ultralytics, the company that developed YOLOv5. YOLOv8 further optimizes the model structure and training strategy based on YOLOv7 to enhance both detection speed and accuracy. Notably, YOLOv8 incorporates a more efficient long-range attention network called Extended-ELAN (E-ELAN), which enhances the model's feature extraction capability. Moreover, YOLOv8 introduces new loss functions, such as VFL Loss and Loss+DFL (Distribution Focal Loss), to improve the model's localization accuracy and category differentiation ability. Additionally, YOLOv8 employs new data enhancement methods, including Mosaic + MixUp, to enhance the generalization and robustness of the model. In the current field of deep learning models, Faster-RCNN, YOLOv5, YOLOv7, and YOLOv8 are all target detection methods known for their high accuracy and advanced algorithms.