## 2 Related Work
### CNN-Based Feature Extraction  
We primarily provide the R-CNN [28] algorithm series and the YOLO [29] algorithm family in the related work to demonstrate the efforts and inadequacies of relevant researchers in order to increase target detection accuracy while taking real-time performance into account. At the same time, it demonstrates that one of the primary causes of detection accuracy loss is downsampling. When it comes to extracting image information, the convolution neural network provides a lot of advantages. Many scientists are working to improve the topology of the convolutional network used to extract picture characteristics. The R-CNN uses a selective search strategy to find anchor boxes before utilizing the pre-trained model to extract characteristics from each one. Fast R-CNN [30] extracts R-CNN image features using a CNN network; then, it uses the ROI pooling layer to produce fixed-length features for each anchor frame. The selective search of the Fast R-CNN network is converted into an RPN (Region Proposal Network) network by Faster R-CNN [31], although RPN is a rough network that does not like small objects. The mask branch is added to the Faster R-CNN network layer structure by Mask R-CNN [32]. In the mask branch, a convolutional network is used. It creates masks using the ROI classifier's positive region as an input, resulting in high precision but poor speed. [PERSON] et al. [29] propose the YOLO (You Only Look Once) framework. Although the detection accuracy could not reach the detection effect of Faster R-CNN, the detection speed reaches 50 frames per second (FPS). A more efficient Darknet-19 is used as the backbone network of YOLOv2 [33]. These measures have effectively improved the detection accuracy of YOLOv2. YOLOv3 [34] absorbs the idea of FPN (Feature Pyramid Network) [35] and performs detection tasks on three feature maps with different scales at three different locations in the network. YOLOv4 [36] is better than YOLOv3 in many indicators. To reduce the loss of image feature information, YOLOv4 introduces PAN-Net (Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network) [37] to make full use of feature fusion. Based on the YOLOv4 backbone, [PERSON] et al. [38] studied the factors affecting the receptive field, used ConvDV to replace ordinary convolution, and increased the number of short circuits and stacks, which increases the receptive field. In addition, in order to make the YOLO algorithm have a smaller volume and higher real-time performance, some small versions of the YOLO algorithm are proposed such as YOLOv3-tiny [39], YOLOv4-Tiny [40], YOLOv5 [41; 42], PP-YOLO [43] based on YOLOv3, \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Model** & \
\begin{tabular}{c} **Yes or No** \\ **CNN-Based** \\ \end{tabular} &
\begin{tabular}{c} **One or** \\ **Two Stage** \\ \end{tabular} & **Loss Description** & **Backbone** & **Main Strategy** \\ \hline YOLOv6 & Yes & One & SIOU in anchor & EfficientRep, Rep-PAN & Introducing Repvgg [22]. The backbone and neck are redesigned based on the idea of hardware perception. \\ \hline YOLOv7 & Yes & One & CIOU in anchor & ELAN-Net [23] & Putting forward Extended-ELAN based on ELAN [23], model scaling based on concatenate model. \\ \hline YOLOv & Yes & One & Explicit loss, implicit loss & YOLOv4-CSP [24] & Applying implicit knowledge hidden in CNN. \\ \hline YOLOv & Yes & One & IOU in anchor & Darknet53 & Adding Decoupled Head, adopting Music and Mixup data enhancement based on YOLOv3. \\ \hline PP-YOLO & Yes & One & IOU Aware branch & ResNet50-vd-don & Detection Neck adopting FPN. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._PP-YOLOv2 [44], YOLOX [45], YOLOr [46], YOLOv6 (there is no paper), and YOLOv7 [47]. The above small YOLO algorithm series means that the smaller the volume, the faster the detection speed, and it also means that the detection accuracy is lower, while the highest detection accuracy among them is 50.3%.