# Swin-YOLO for Concealed Object Detection in Millimeter Wave Images
## 1 Introduction  
With the increasing emphasis on public safety in modern society, security inspection has become a necessary part of social development. It requires the fast and accurate detection of hazards from the source to minimize unnecessary losses. The traditional methods of security inspection are widely used, but they generally suffer from different defects: optical methods, as an early detection method, have a short wavelength and are unable to penetrate clothing, which makes it difficult to detect hidden objects; metal detection instruments can only identify metal targets of certain sizes and are unable to identify non-metallic items [1], such as fire sources; and X-ray security inspections, although penetrating, will generate harmful ionizing radiation to the human body, affecting human health [2]. These negative factors have greatly affected the effectiveness and reliability of security checks, resulting in a hidden danger for safety. In recent years, millimeter-wave (MMW) imaging technology has received widespread attention in the field of security inspection, owing to the excellent penetration characteristics and harmlessness of MMW radar [3; 4]. MMW radar has a wide-ranging detection capabilityfor both metallic and non-metallic objects [5]. The MMW imaging system emits non-ionizing electromagnetic waves [6] and receives reflected energy from various parts of the human body and their surface attachments. MMW images are generated based on the changes in these reflected energies. MMW imaging technology can accurately reflect the types and distribution of human concealed objects, so the object detection of concealed object images naturally becomes the major focus of current research. Figure 1 illustrates the MMW imaging principle. Traditional methods for detecting concealed objects in images rely on geometric feature matching to segment the object from the body region [7], followed by an analysis of the target area for detection and classification. However, these methods are limited to analyzing single images and have poor real-time performance, which cannot meet the demands of modern security inspection. With the rapid development of deep learning, deep learning-based object detection methods have been widely applied in the concealed object detection for the MMW image. For instance, [PERSON] et al. [8] employed a deep learning-based deformable body partition model to combine suspicious target information with body part information and detect concealed objects, while [PERSON] [9] proposed a convolutional neural network (CNN) with pose segmentation functionality to divide human images into body part images, and then detected clutter anomalies in body parts to identify objects, both of which achieved remarkable performance and effectively addressed the challenges posed by traditional methods. [PERSON] et al. [10] proposed a detection framework using multi-level features information from cross-section sequences for detecting MMW concealed objects. The framework consists of a CNN and a Long Short-Term Memory (LSTM) network, achieving more effective feature fusion. Although achievements have been made in MMW image concealed object detection based on deep learning, it still faces a series of problems. Firstly, in terms of image quality, the difference in the intensity of MMW reflection between the human body area and the concealed objects is not obvious, and there is insignificant difference in image grayscale [11]. This makes it difficult to distinguish the texture of human body parts and concealed objects. Moreover, MMW radar imaging is affected by environmental and random noise, resulting in low imaging signal-to-noise ratio. The reduction in available information in the concealed object image area, as well as the complex image information and noise impact, all have an impact on the detection accuracy of the network.