# A Lightweight Uav Swarm Detection Method Integrated Attention Mechanism
## 2 Related Work
### Related Work for UAV Detection  
Object detection algorithms based on deep learning are mainly divided into two categories: two-stage and one-stage detectors. In the former approach, first, a region proposal network is used to estimate a candidate object bounding box. Then, in the second stage, the network extracts features from each candidate box and performs classification and bounding box regression. In this manner, several methods such as R-CNN [16], Fast R-CNN [17], and Faster R-CNN [18] have been proposed. The latter object detector uses a single deep neural network with a regression strategy to directly classify and detect objects. It should be noted that, in this approach, the process of region proposal is avoided. In this manner, several methods such as the You Only Look Once (YOLO) series, Single Shot Detector (SSD) [19], and RetinaNet [20] have been proposed.  
With the rapid development of computer vision technology, researchers have carried out a lot of research on image-based UAV detection algorithms. [PERSON] et al. [21] introduced an algorithm based on YOLOv3 into UAV object detection for the first time. In the prediction process, the last four scale feature maps are adopted to conduct multi-scale prediction to enrich the texture and contour information. At the same time, the size of the UAV in four scales feature maps is calculated according to input data, and then the number of anchor boxes is also adjusted. This approach improves the accuracy of small object detection while ensuring speed. [PERSON] et al. [22] proposed a UAV detection network named TIB-Net, integrating a structure called cyclic pathway into the existing efficient method Extremely Tiny Face Detector (EXTD) to enhance the capability of the model to extract effective features of small objects. Furthermore, they integrated a spatial attention module into the backbone network to better locate small-size UAVs and further improve detection performance. [PERSON] et al. [23] integrated the attention mechanism module into the PP-YOLO detection algorithm and introduced the Mish activation function to eliminate the gradient disappearance problem in the back-propagation process, which significantly improved the detection accuracy. [PERSON] et al. [24] proposed a multi-UAV detection network named FastUAV-NET based on YOLOv3-tiny that can be used for embedded platforms. By increasing the depth and width of the backbone network, local and global features are extracted from the input video stream, providing higher detection accuracy and saving computing time. [PERSON] et al. [25] replaced the backbone with a lightweight network Efficient-lite based on YOLOv5s to reduce the number of parameters of the model, introduced adaptive spatial feature fusion technology to balance the loss of accuracy caused by simplifying the network model, and, finally, introduced a constraint of angle into the original regression loss function to avoid the mismatch between the prediction frame and the real frame orientation during the training process in order to improve the speed of network convergence. [PERSON] et al. [26] used the Se-ResNet as a feature extractionnetwork by introducing the SeNet attention mechanism into the backbone to improve the correlation between feature channels and enhance the features of the target to solve the problem of UAVs in low-altitude airspace being submerged in complex background clutter. The differences between the proposed method and the existing UAV detection frameworks are shown in Table 1.