## 1 Introduction
### Object Detection  
As we all know, object detection is based on image classification. After AlexNet [7] proposed a deep convolutional neural network for image classification to achieve a breakthrough in performance, many CNN-based methods of object detection began to emerge. SPPNet [8] proposed spatial pyramid pooling, so that the size of the image no longer limited the input of the CNN, which also improved the recognition performance for multi-scale targets. Due to the long training time and large memory consumption of R-CNNs, a fast R-CNN [9] was proposed. Faster R-CNN [10] incorporated region proposal networks(RPNs) by sharing convolutional layers with SOTA object detection networks and improved the training speed of the network. However, the CNN-based deep model could not identify the negative samples that the AI model had difficulty distinguishing at that time. In order to solve this problem, online hard example mining (OHEM) [11] and focal loss [12] were proposed. By sorting input RoIs, OHEM could select hard examples from low-loss values. Focal loss solved the problem of sample imbalance by tilting the loss function to make the model focus on hard samples, which are difficult to classify. The previous object detection network was based on a single high-level feature, which made it challenging to solve multi-scale variant problems, especially with small targets. The use of image pyramids led to a surge in calculations. Through a unique feature pyramid network, [13] avoided a high number of calculations and solved multi-scale problems[PERSON] In order to further enhance the feature extraction ability of the model, [PERSON] [14] improved the FCN by fusing local features with global features and proposed CoupleNet. The previous object detection algorithms obtained the final results by implementing NMS through Intersection over Union (IOU)[PERSON] Soft NMS [15] receives the IOU of two candidates through a Gaussian function, enacts different degrees of punishment, and modifies the target's confidence in combination with the strength of the punishment. Cascade R-CNN [16] was an essential improvement in object detection. Through multiple cascaded detectors and different IOU thresholds, Cascade R-CNN improved the mAP by two to four points in almost all R-CNN-based networks. [PERSON] [17] explored the role of a fully connected head and convolution head in object detection in detail and proposed the double-head method, which involved a fully connected head focusing on classification and a convolution head for bounding box regression. [PERSON] [18] proposed a weighted bi-directional feature pyramid network (BiFPN) and a compound scaling method to enhance the feature extraction capability of models[PERSON] [PERSON] [19] proposed a label allocation method based on an optimization strategy, which allowed the network to choose the number of anchors corresponding to each ground truth, thereby avoiding the anchor allocation problem caused by setting in advance[PERSON] [PERSON] [20] proposed a new dual weighting paradigm, which could achieve a better loss by giving different weights to positive and negative samples. Since [PERSON] proposed YOLOv1 [21], single-stage object detection models have gradually become a research hotspot. The main difference between single-stage and two-stage detectors is that the single-stage model only needs to input images into the network to obtain the end-to-end result[PERSON] However, the two-stage model needs to propose a candidate region and then perform object detection, which greatly affects the running speed of the detector. In the beginning, the detection accuracy of the single-stage model was low, but with the deepening of research, a large number of excellent works have emerged: for example, the YOLO series [21; 22; 23; 24], SSD [25], and RefineDet [26].