## 1 Introduction  
Object detection aims to retrieve the classes of specific targets along with their size and location in visual data, such as images or videos. In recent years, the rapid evolution of many-core processors has accelerated the development of object detection algorithms based on deep learning techniques. As the most common deep learning framework, convolutional neural network (CNN)-based models exhibit powerful feature extraction capabilities, resulting in state-of-the-art object detectors, such as SSD [1], EfficientDet [2], and YOLO series [3, 4, 5]. These existing object detection models are optimized for detecting common objects from visible-spectrum RGB images using large-scale datasets such as ImageNet [6], PASCAL VOC [7], and MS COCO [8]. In terms of real-world usage, a variety of object detection applications based on task-specific annotated datasets are being widely adopted. The powerful feature extraction capability of deep learning networks combined with large-scale datasets has led to the high detection performance and robustness of models, allowing the object detection models to be one of the most practical usages of deep learning algorithms. It is thus not only widely used in our daily life, such as in autonomous driving [9], pedestrian detection [10], and face detection [11], but it is also used in industries and military applications, such as defect detection [12], robot automation [13], and target detection [14]. However, in such areas where a large-scale dataset is unavailable, the data-driven nature of deep learning algorithms acts as a weakness. In this paper, the proposed framework aims to solve a small object detection task in infrared images, where datasets comprised of elements satisfying both \"small\" and \"infrared\" conditions are insufficient. Although this task is very important in scenarios such as maritime surveillance, early-warning systems, remote sensing and medical imaging [15; 16], there have been few studies on this topic due to the insufficiency of datasets. In terms of small object detection, several works have been proposed, which direct the processing towards candidate areas of the input image [17; 18] or extract different scales of features [19], where both approaches are based on modifications of the baseline network architecture. In the case of object detection in infrared images, similar approaches [20; 21] have been proposed that optimize the network architecture to reflect characteristics of infrared images. In both cases, the proposed methods assume that there exist sufficient datasets to train the network, which is not applicable to our objective. Synthesizing datasets can be one of the approaches to overcome the data shortage [22]. However, domain shifts should be considered because a distribution mismatch between data of the source and target domains may produce a significant performance drop. In our task, where these multiple challenges coexist, it is impossible to directly apply the methods listed above. Therefore, we aim to solve this complex problem via a domain adaptation strategy that can achieve high accuracy with robustness in multiple domains without adding a complex auxiliary network structure or excessive calculations. Human beings possess an inherent ability to perceive objects in images or videos almost flawlessly. Deep learning-based object detection models are designed to mimic this ability through the feature extraction capability of CNNs. This implies that every model focuses on the shape and visual characteristics of each visual element and combines the cues to detect the location and category of the target object. Therefore, if a model is able to extract similar visual features from multiple domains (visible light, infrared, colored image, grey-scale images, etc. ), its ability to detect objects can be well adopted to other domains where objects share similar visual shapes. Based on this concept, we propose a domain adaptation framework to force a model to extract similar features regardless of the source domain.