## 2 Related Work
### Deep Neural Network  
Deep learning approaches are now being applied in CV applications due to advancements in computer hardware and neural network (NN) algorithms [14; 15; 16]. Research on deep learning backbone networks has achieved great success, i.e., AlexNet [17], VGGNet [16], GoogleNet [18], ResNet [19], DenseNet [20], and EfficientNet [21]. Target identification techniques in deep learning are either one- or two-stage systems. SSD [22], RFBNet [23], and YOLO [24; 25; 26; 27] are examples of one-stage detectors. The main two-stage target detector is Faster-RCNN [28; 29]. [PERSON] et al. [30] explicitly employed context to fuse characteristics to enhance the efficacy of small-size object detection. [PERSON] et al. [31] introduced the kind of original dataset. In the meantime, they proposed a model by enhancing the Faster R-CNN method. In the streamlined YOLOv2 model, some convolutional layers are removed, reducing the computational complexity while maintaining accuracy in detecting ship images of SAR [32]. [PERSON] et al. [33] proposed a combination of squeeze stimuli and accelerated the Faster R-CNN architecture. [PERSON] et al. [34] streamlined feature extraction using a U-net network as the foundation to find ship targets. [PERSON] et al. [35] developed HyperLi-Net, a high-accuracy and fast SAR ship detection system.  
Over the last decade, object identification in natural images has made significant progress. When ordinary image target detection methods are directly used for satellite image target detection, the results are far from satisfactory. This is because of the inherent variations in object size and orientation brought by satellite images' eagle-eye viewpoint. Satellite images have a complex background and a tiny object area [36]. An improved YOLOv3 object detection model combining data augmentation and structure optimization is proposed in this study [37]. Furthermore, the YOLOv7 model applied to the LB dataset outperformed existing reported studies. This study illustrates the potential of the YOLOv7 model for weed detection [38]. [PERSON] et al. [39] implemented a YOLOv three-based detection model of infusion. All the above research works demonstrate the excellence of the YOLO algorithm in the field of deep learning. They also provide a research basis for us to adopt improvements based on the YOLOv4 model.  
The YOLOv4 detector, shown in Figure 1, utilizes CNN and comprises four components. In object detection algorithms, the \"backbone\" network could extract the basic features of input images. Cross Stage Partial (CSP) Darknet53 serves this role as the \"backbone\" network for YOLOv4. The \"Neck\" module is the third part of top-down and bottom-up propagation with semantic information features through the spatial pyramid pooling (SPP) module. Following the SPP module, the top-down feature pyramid network (FPN) and bottom-up feature pyramid network (PANet) modules are coupled. The detecting head of YOLOv3 is still used in the prediction section. To calculate the distance between the prediction box and the ground truth, the Complete Intersection over Union (CIOU) loss function is employed. To estimate the position of the target frame, the Distance Intersection over Union (DIOU) Non-Maximum Suppression (NMS) approach is adopted.  
Figure 2 shows the specific modules in the YOLOv4 structure diagram. * represents the multiplication sign. CBM: This component includes operations of convolution (conv), batch normalization (BN), and mish activation function; CBL: This component includes operations of conv, BN, and Leaky_relu activation function; Resunit: This module is similar to the residual unit used in the Resnet model to help build deeper networks; CSPX: These are a CBM and X Resunits in series; SPP: This is a multiscale fusion module through 1 \(\times\) 1, 5 \(\times\) 5, 9 \(\times\) 9, and 13 \(\times\) 13 maximum pooling methods. Figure 3 represents 3D process of feature extraction operation by FPN and PANet.