## 2 Related Works  
### YOLOv5s  
YOLO series is a single-stage deep learning method, mainly including YOLOv1, YOLOv2, YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7, and YOLOv8. The YOLOv1 [22] algorithm divides the image into several grids for regression tasks and uses a grid containing the center point of the object for classification tasks. Its network architecture is composed of 24 convolutional layers and 2 fully connected layers. On the basis of YOLOv1, YOLOv2 [23] uses batch normalization to eliminate gradient disappearance and gradient explosion problems, replaces the full connection layer with an anchor frame, and introduces the pass-through layer to retain more feature information. Compared with YOLOv1, YOLOv2 consists of 19 convolutional layers and 5 maximum pooling layers, which improves speed and accuracy. YOLOv3 [24] uses the residual network to form the Darknet53 network as the backbone feature extraction network according to the idea of the FPN feature pyramid and uses three different sizes of detection heads to detect samples of different sizes. YOLOv4 [25] has made a comprehensive improvement to the network structure, using Mosaic data enhancement to enhance the input data. The SPPNet network and PANet network are used to construct the neck layer. The activation function is replaced with the Mish function. The CIOU_Loss loss function is used to calculate the regression loss of the target box. Compared with YOLOv3, the target recognition ability of YOLOv4 is greatly improved. On the basis of YOLOv4, YOLOv5 [26] has added some new improvement ideas. YOLOv5 adds adaptive image filling and adaptive anchor frame calculation to the input part to process the data, which increases the diversity of the data and improves the accuracy. CSPDarkNet53 network is mainly used in the backbone network, which introduces the Focus structure and CSP structure to improve the speed without losing training accuracy. The output end uses the GIOU_Loss loss function for regression, and the weighted NMS operation filters multiple target anchor frames to improve the accuracy of target recognition. YOLOv6 [27] is designed by the [PERSON] team based on the idea of RepVGG and uses the new SIoU Loss as the position loss function, which outperforms other algorithms in speed and accuracy. The main improvement of YOLOv7 [28] is the introduction of an efficient layer aggregation network into the backbone network; the neck network is mainly composed the of SPPCSPC module, MP module, ELAN-M module, and reparameterized module. While the accuracy of YOLOv7 is improved, the network structure has become more complex. The main improvements of YOLOv8 [29] are as follows: the backbone uses the concept of Cross Stage Partial (CSP) to split the feature map into two parts; the C2f module is used in YOLOv5 instead C3 module; and the neck uses multi-scale feature fusion of images, which can further improve the accuracy and the lightweight feature. Based on the comprehensive consideration of the accuracy and real-time performance of all models of the YOLO series, YOLOv5 is a widely used model and is selected as the basic network for improvement. The network structure diagram is shown in Figure 1.  
YOLOv5 has four versions: YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. The main difference is that the depth multiple and width multiple of the model are different, as shown in Table 1.  
In the YOLOv5 network, DM affects the number of network application bottleneck components, that is, the length of the network depth. The number of bottleneck components applied to different versions of the YOLOv5 network is different and needs to be determined according to the theoretical repetition number and DM of bottleneck components--that is, the actual repetition number = the theoretical repetition number of bottleneck \(\times\) DM. In the YOLOv5 network, WM affects the number of convolution kernels in the network convolution layer (Conv), that is, the number of channels.