## 2 Related Work
### Concealed Object Detection  
Another problem involves the detection of concealed objects which are perfectly embedded in their backgrounds [25]. The detection of concealed objects is common in security inspections, which are employed to identify dangerous items hidden beneath clothing [25]. Studies have focused on passive terahertz imaging [26], a technology that uses terahertz (THz) radiation to capture images for the detection of concealed objects. [PERSON] et al. [27] presented a real-time algorithm for the computer processing of passive THz images aimed at detecting concealed objects. A correlation function is used between the THz image and a standard image. It triggers an alarm if the correlation function value exceeds a certain level. As part of this scope, [PERSON] [28] compared two deep learning-based processing methods (YOLOv3 and R-FCN) for the detection and recognition of concealed objects. R-FCN outperformed YOLOv3. A multi-scale filter and geometric transformation matrix (MSFG) augmentation for passive terahertz (THz) images is proposed in [29]. MSFG augmentation significantly improves detection accuracy in passive THz images. YOLOv5I-MSFG shows the best performance, with an accuracy rate of 93.49% for one-class detection and 90.76% for two-class detection. [PERSON] et al. [30] introduced an advanced clustering algorithm that enhances concealed object detection by achieving a 90.38% recall rate and 94% precision. Finally, [PERSON] et al. [31] proposed replacing the SSD backbone network with a more representative residual network to reduce network training difficulty. The SSD algorithm improved its accuracy from 95.04% to 99.92% in experiments and outperformed other algorithms such as Faster R-CNN and YOLO. Passive millimeter wave technology is a non-invasive imaging technique that detects concealed objects by capturing millimeter wave radiation emitted by objects and their surroundings [32]. In several studies, this type of image is analyzed using deep learning methods such as YOLOv3 [32] and a one-stage anchor-free detector combining a convolutional neural network (CNN) and transformers [33]. The active millimeter wave imaging technique is also used for the detection of concealed objects and involves the use of an external source that emits millimeter waves in the direction of an object. The use of fast wavelet transforms (FWTs) for segmenting images has an accurate detection rate of 80.92%, with a false alarm rate of 11.78% [34]. For data acquisition, another method uses multiple-input, multiple-output radar antennas, and synthetic aperture radar techniques, as well as an analytical Fourier transform algorithm for image reconstruction. GhostNet_SEResNet56 (lightweight convolutional neural networks) achieved a prediction accuracy of 98.18% [35]. Additionally, [PERSON] et al. [36] presented a normalized accumulation map-based training mechanism. Using binary masks, this method identifies commonly appearing concealed objects and assigns different weights to various locations. With this training mechanism, YOLOv2 shows a 4.43% improvement in mean average precision. An additional innovation in this field can be found in [37], where indirect microwave holography was used in transmission mode to detect and identify concealed objects by analyzing the shapes and sizes of metallic sheets. The self-paced feature attention fusion network (SPFAFN) [38] integrates multi-scale features and uses a hierarchical pyramid attention mechanism. This method combines channel and spatial attention and employs self-paced learning. On real-world datasets, SPFAFN outperforms existing methods. [PERSON] et al. [39] proposed a multi-source aggregation transformer (MATR) that encodes local relationships within each image using a self-attention module, while a cross-attention module models interactions across multiple images from different viewpoints. [PERSON] et al. [40] presented a novel time-domain beamforming algorithm for ultra-wideband microwave imaging using Vivaldi antennas.