# Swin-YOLO for Concealed Object Detection in Millimeter Wave Images
## 2 Related Work
### Concealed Object Detection on Millimeter Wave Images  
Earlier, researchers used hand-designed features to detect concealed objects in MMW images and segment the concealed object part. [PERSON] et al. [34] modeled the hidden object by analyzing the denoised image histogram and the internal temperature distribution, then evolved curves along the isocontours of the image to separate objects from the background. [PERSON] et al. [35] extracted geometric feature vectors from binary segmented images using preprocessing, Principal Component Analysis (PCA), and size normalization to separate concealed objects from body regions. [PERSON] et al. [36] used a multi-level segmentation approach containing the Expectation-Maximization (EM) algorithm to detect and segment concealed object regions. However, due to the small size of the feature regions and the limited number of feature parameters, hand-designed features were unable to fully and effectively express the characteristics of concealed objects.  
With the development of deep learning, researchers have adopted CNN for MMW concealed object image detection. This is because CNNs can encode features and implicitly learn low-order to high-order features from training data, making the extracted features more discriminative. [PERSON] et al. [37] proposed a context-embedded object detection network and applied dilated convolution to expand the resolution of feature maps. By extracting detailed features and contextual information, this network improves the detection performance of small objects. [PERSON] et al. [8] proposed a deformable body partition model that divides MMW human body images into multiple regions and detects hidden objects for different body regions, dynamically tracking objects from different angles using spatiotemporal context information. [PERSON] et al. [9] used the clutter features of MMW human body segmentation images for deep learning training, and the clutter anomaly detector had a stronger generalization performance. [PERSON] et al. [38] proposed a two-stage MMW human body concealed object image detector. The network improved the fusion relationship between adjacent feature maps in the FPN and used multi-angle images to optimize the detection results. [PERSON] [39] proposed a domain adaptive detector for MMW image concealed objects. The network is composed of a detection module with hierarchical attention units and an unsupervised domain adaptive module. The detection module fused low-level and high-level features, and the domain adaptive module extracted the domain differences of features. [PERSON] et al. [40] proposed a multi-path feature pyramid (MPFF) model and an improved residual block distribution to enhance the recognition accuracy. [PERSON] et al. [41] proposed a concealed object detection method based on normalized accumulation maps (NAM), which can reveal frequently occurring concealed object locations. Most of the methods in the above-mentioned research are based on two-stage detectors. However,MMW human body concealed object detection requires high real-time performance. Thus, it is necessary to explore object detectors that balance real-time performance and accuracy. Table 1 shows the comparison of the methods above. Detailed information on the key methods, models, average precision, and inference time used in each of the studies are provided.