# Swin-YOLO for Concealed Object Detection in Millimeter Wave Images
## 2 Related Work  
### Object Detection  
The object detection framework based on convolutional neural networks (CNN) is currently a hot topic in computer vision research. Researchers have widely classified it into two-stage detection methods and single-stage detection methods. The two-stage detection, also known as region proposal-based detection, achieves a high detection accuracy and mainly includes the Region-CNN (RCNN) series. In the first stage of the RCNN [13] network, a selective search algorithm is used to generate candidate regions on the input image, and convolutional neural networks are used to extract features from these candidate regions, ensuring sufficient precision and recall. In the second stage, the objects within the candidate regions are classified and regressed. The Fast-RCNN [14] algorithm integrates RCNN and a Spatial Pyramid Pooling Network (SPP-Net) [15] and uses Region of Interest-Pooling (ROI-Pooling) to map candidate region feature windows of different sizes onto feature vectors of the same size, thus unifying the input size. Faster-RCNN [16], on the other hand, employs the Region Proposal Network (RPN) in the first stage and the anchor mechanism to generate object candidate regions, and classifies the objects within the anchor in the second stage, further improving the detection speed and accuracy of the network. Although the RCNN network has advantages in accuracy, the two-stage object detection network still cannot achieve real-time detection.  
Single-stage detection methods consider object detection as a one-time problem and use regression methods to obtain the coordinates of object bounding boxes and class confidence. This enables the high-speed positioning and classification of objects in an end-to-end manner. Single-stage methods mainly include the Single Shot Detector (SSD) [17] and the YOLO series algorithms, where SSD introduces the RPN structure and uses multi-scale feature maps to detect objects. YOLO [18] is the landmark algorithm for single-stage object detection, establishing the main idea of the subsequent framework, using GoogLeNet as the feature extraction network. YOLOv2 [19] improves the backbone network by using Darknet-19 to extract object features and adding batch normalization layers to each convolutional layer to reduce the sensitivity of the model to parameters. YOLOv3 [20] introduces a residual network module and uses Darknet-53 as the backbone network, predicting objects separately with multi-scale feature maps, which improves the detection performance for objects with large scale differences. YOLOv4 [21] introduces Mosaic data augmentation at the input end, the backbone network uses the Cross Stage Partial Network Darknet-53 (CSPDarknet-53) to avoid the problem of repeated gradient information, and it uses Dropblock to randomly reduce the number of neurons to simplify the network operation. Also, YOLOv4 uses a Feature Pyramid Network (FPN) [22] combined with a Path Aggregation Network (PANet) [23] structure for multi-scale feature extraction to fully integrate multi-scale features, and achieves higher accuracy than YOLOv3 at the same speed. The strategy of not relying on region detection greatly improves the detection speed of the single-stage methods, balancing accuracy and detection speed well. YOLOv5 [12], as the culmination of the YOLO series, has all the advantages of this series of networks, achieving further improvements in detection accuracy and speed. It includes four models: YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. With increasing network depth and width, the extraction and fusion ability of features are continuously enhanced, which is suitable for different scenarios and meets the requirements of modern object detectors. Although there are many new versions of the YOLO series [24; 25; 26], the specific architecture tends to be consistent with the design philosophy of YOLOv5. Therefore, this article adopts YOLOv5l as the basic framework for object detection.