# Swin-YOLO for Concealed Object Detection in Millimeter Wave Images
## 1 Introduction
Secondly, with regards to detecting targets, the concealed objects in MMW images are small, their size is much smaller than the human body, and the resolution of concealed objects is low, resulting in insufficient feature information [10]. In deep learning networks, the features of small objects gradually decrease as the network deepens, bringing great difficulties to detection. Figure 1: The model of millimeter wave imaging principle. For the problems in the MMW image of detecting hidden objects, a novel concealed object detection method based on the YOLOv5 [12] model is proposed. YOLOv5 was selected as the framework for its performance in harmonizing accuracy and speed, and it concurrently ensured engineering forward compatibility. Moreover, a series of enhancements were introduced. First, to make the YOLOv5 network have the ability of global context modeling and have better performance in detecting complex concealed object features, we have made improvements to the neck network and added Local Perception Swin Transformer Layers, which can enhance the context information connection of the feature map through a self-attention mechanism and make better use of local information through a Local Perception Module. Second, we added a detection branch for small objects in the shallow layers of the network and introduced a bidirectional feature pyramid network (BiFPN) in the path aggregation neck. Through the bidirectional cross-scale connections and fast normalization fusion, the feature fusion capability of the network was enhanced. The small object detection branch can retain the feature information of small concealed objects to the greatest extent, thus solving the problem of feature information loss caused by neural network down-sampling. Finally, we added the Coordinate Attention Module at the connection between each detection head and the neck network, which makes the network more sensitive to positional information and focuses on critical image information. Experimental results showed that, in the detection of concealed objects in MMW concealed images, the performance of our proposed object detector was superior to the original YOLOv5 network. The main contributions of this paper can be summarized as follows:  
1. More comprehensive contextual information is utilized through improved Swin Transformer. The Local Perception Swin Transformer Layers are added to the neck network of YOLOv5, which enhances the network's ability to model feature context through the introduction of a window self-attention mechanism and Local Perception Module. 2. The features of small targets are sufficiently extracted through more refined feature extraction and fusion networks. Additional feature fusion layers and prediction heads for small concealed objects are added to YOLOv5, enhancing the network's detection performance for MMW targets. BiFPN is introduced to strengthen the network's feature fusion ability. 3. The key features of the concealed object are fully used. The lightweight Coordinate Attention Modules are added to the connection between the neck and detection heads of YOLOv5, which improves the extraction and utilization of critical features. 4. We collected an MMW concealed object image dataset to support the training of different network models and test the detection performance of concealed objects. The dataset includes five classes of suspicious items and a total of 82,014 MMW images.