## 2 Related Work  
YOLO was first proposed by [PERSON] et al. in 2015, and the official version has been updated from YOLOv1 to YOLOv3. It is worth noting that YOLOv4 and YOLOv5 are not official versions. The YOLO series network directly regresses the information of a grid cell bounding box to the final feature map, yielding three prediction values for each bounding box: (1) the probability of the object being in the grid; (2) the coordinates of the bounding box, and (3) the object class and its probability. For each grid cell, the predicted values include five parameters: x, y, w, h, and cf, where x, y, w, and h denote the x and y coordinates, height, and width of the center point of the enclosing box, respectively, and cf denotes the confidence of the bounding box. Therefore, the loss function of the whole network can be written as shown in Equation (1):  
\[\begin{split}\text{loss}&=\text{r}_{\text{coord}} \sum_{i=0}^{\text{s}^{2}}\sum_{j=0}^{\text{B}}\text{g}_{\text{ij}}^{\text{obj }}[(\text{x}_{i}-\text{\dot{x}}_{i}^{j})^{2}+(\text{y}_{i}-\text{\dot{y}}_{i}^ {j})^{2}]\\ &+\text{r}_{\text{coord}}\sum_{i=0}^{\text{s}^{2}}\sum_{j=0}^{ \text{B}}\text{g}_{\text{ij}}^{\text{obj}}[(\sqrt{\text{w}_{i}^{j}}-\sqrt{ \text{\dot{w}}_{i}^{j}})^{2}+(\sqrt{\text{h}_{i}^{j}}-\sqrt{\text{\dot{R}}_{ i}^{j}})^{2}]\\ &-\sum_{i=0}^{\text{s}^{2}}\sum_{j=0}^{\text{B}}\text{g}_{\text {ij}}^{\text{obj}}[\text{\dot{C}}_{i}^{j}\text{log}(\text{C}_{i}^{j})+(1- \text{\dot{C}}_{i}^{j})\text{log}(1-\text{C}_{i}^{j})]\\ &-\text{r}_{\text{noobj}}\sum_{i=0}^{\text{s}^{2}}\sum_{j=0}^{ \text{B}}\text{g}_{\text{ij}}^{\text{noobj}}[\text{\dot{C}}_{i}^{j}\text{ log}(\text{C}_{i}^{j})+(1-\text{\dot{C}}_{i}^{j})\text{log}(1-\text{C}_{i}^{j})]\\ &-\sum_{i=0}^{\text{s}^{2}}\text{g}_{\text{ij}}^{\text{obj}} \sum_{c\in\text{classes}}[\text{\dot{P}}_{i}^{j}\text{log}(\text{P}_{i}^{j})+( 1-\text{\dot{P}}_{i}^{j})\text{log}(1-\text{P}_{i}^{j})]\end{split} \tag{1}\]  
In the equation, s\({}^{2}\) represents the number of grids, B represents the number of anchors, and \(\gamma_{\text{ij}}^{\text{obj}}\) represents whether the corresponding anchor box is responsible for detecting the object. If it is responsible, \(\gamma_{\text{ij}}^{\text{obj}}\) is 1; otherwise, it is 0. \(\text{\dot{C}}_{i}^{j}\) represents the ground truth, which is determined by whether or not the bounding box of the grid is responsible for predicting an object. If this is the case, \(\text{\dot{C}}_{i}^{j}\) is 1; otherwise, it is 0. When calculating the multi-classification loss, we regard it as multiple two-classification tasks. For each category, the ground truth \(\text{\dot{P}}_{i}^{j}\) is 1 if the object belongs to this category; otherwise, it is 0, and the prediction \(\text{P}_{i}^{j}\) indicates the probability that the object belongs to this category. Our approach follows the loss function of YOLOv3, which will not be described in subsequent sections.  
The backbone of YOLOv3 is Darknet53 [36], which downsamples each input image five times, with the last three downsampled layers transmitted to the detection layer for object detection after feature fusion. The structure of the YOLOv3 is shown in Figure 1. For a 416 \(\times\) 416 input image, the three scales of the detection layers are 13 \(\times\) 13, 26 \(\times\) 26, and 52 \(\times\) 52, which are responsible for detecting objects at different scales. The deep layer contains a large amount of semantic information, while the shallow feature-mapping layer contains a large amount of fine-grained information. Therefore, the network uses a feature pyramid to perform feature fusion, where the downsampled 32-fold feature map is first upsampled to the same size as the downsampled 16-fold feature map, and then, the feature maps are cascaded together. Similarly, the same process is performed for the downsampled 16-fold feature map and the downsampled 8-fold feature map.