## 2 Related Works  
### YOLOv5 Network  
YOLOv5 is a single-stage target detection algorithm proposed by Ultralytics [32] that transforms the target bounding box positioning problem into a regression-based problem where a single neural network is used to predict both the bounding box and the category of the target. [PERSON] et al. [33] utilized MobileNetv3 and genetic algorithm (GA) to improve YOLOv5, while incorporating the model with augmented reality (AR) technology. They proposed a novel compatible detector for equipment anomaly detection in hydropower plants. [PERSON] et al. [34] optimized the YOLOv5 model by integrating the attention mechanism CBAM and adjusting the loss function, and their model was used for defect detection in citrus epidermis. [PERSON] et al. [35] proposed an improved forest fire detection model based on YOLOv5 from the perspective of multi-scale features, which achieved high detection accuracy and speed. Compared to YOLOv4, YOLOv5 further improves on the YOLOv4 algorithm to achieve faster inference and higher accuracy. In addition, the small amount of weighted data in the YOLOv5 network makes it easy to deploy in devices with limited memory and power consumption requirements.  
The network structure of YOLOv5 is divided into four parts: input, backbone, neck, and head. The input represents the input image. All three operations of Mosaic data enhancement, image size processing, and adaptive anchor box calculations are used by the model in this phase to improve the training speed and network accuracy. The backbone is a convolutional neural network for extracting multi-scale image features. The neck has a characteristic pyramid structure with Feature Pyramid Network (FPN) [36] and Path Aggregation Network (PAN) [37]. Shallow graphical features and deep semantic featuresare combined through this structure to improve the flow of feature information on the network. Meanwhile, Image feature maps are used in head to generate bounding boxes and predict the class of the object.  
The YOLOv5 network model consists of five different sizes, the YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5x, and YOLOv5n [38]. Backbones of different sizes are acquired by adjusting the depth_multiple and width_multiple of parameters. YOLOv5s is the simplest version of these versions since the depth_multiple and width_multiple are 0.33 and 0.50, respectively. The structure of the network model for YOLOv5s is shown in Figure 1.  
In YOLOv5, the backbone network consists of Focus module, convolution, Cross Stage Partial Bottleneck with three convolutions (C3), and Spatial Pyramid Pooling (SPP). The Focus module splits the feature map into four independent feature layers, and performs stacking and convolution operations to effectively improve the quality of image feature extraction. The C3 module, which consists of three convolutions and several bottlenecks, is built based on a cross-stage partial network (CSPnet) [39]. SPP is a spatial pyramid pooling layer that further increases the Receptive-field of the feature map. The Conv module used in the network is composed of standard convolution (SC), batch normalization, and Sigmoid Linear Unit (SiLU) activation layers. The parameters of the convolution function are input channels, output channels, kernel, and stride.