## 2 Related Research  
Most intelligent surveillance systems utilize algorithms based on Regions with CNN features (R-CNN) to perform object detection and dangerous situation recognition with images captured from cameras. In [1], [PERSON] and [PERSON] proposed a system using the Fast R-CNN model to detect two types of weapons: guns and knives. When the system detects a potential crime situation, it sends an SMS message to a human supervisor to take necessary actions. [PERSON] et al. proposed a video surveillance method for indoor environments based on a well-trained Mask R-CNN model using the Microsoft COCO dataset [2]. In the above methods based on R-CNN, there are several common processes for object detection. First, a regional proposal process is performed to suggest areas where objects are likely to be located. After the regional proposal process, a feature extraction process is followed for the proposed areas. Finally, for each proposed area, object classification or regression to estimate the object's location is performed. Feature extraction, object classification, and regression have undergone little modification since they are basic processes of the R-CNN model. On the other hand, there have been various approaches to improve the regional proposal process. The early R-CNN method used the selective search algorithm [3]. The algorithm uses the brightness of pixels to create components and group them to find regions [4]. This method has the disadvantage that requires a long time for many calculations, such as having all found regions pass a CNN model and running a Support Vector Machine (SVM) for each class for each result. Two improved methods, Fast R-CNN and Faster R-CNN, applied the Regional Proposal Network (RPN) instead of the selective search algorithm [5,6]. Figure 1 shows the architecture for Faster R-CNN to perform object detection. In Figure 1, the feature map extracted from the image is processed through a regional proposal network and regions are proposed. The feature map is then ROI pooled according to the proposed regions and classified by regions in the classifier. Because the RPN shares a network with the backbone, it can improve operational efficiency by omitting redundant training. Comparison results using the VGG16 backbone network showed that the time required for the regional proposal process was significantly reduced by the application of the RPN. This is evaluated as having advanced to a level where the real-time object detection is possible [5]. More recently, one-stage detectors have emerged to facilitate real-time inference in practical systems. Unlike their two-stage counterparts, these detectors perform regional proposal and classification simultaneously, eliminating the need for a separate Region Proposal Network (RPN). DETR and YOLO (You Only Look Once) represent prominent examples of this one-stage detection paradigm [7; 8; 9; 10]. DETR (Detection Transformer) introduces an innovative approach by applying the Transformer architecture to computer vision tasks, departing from traditional CNN-based object detection methodologies. This model reconceptualizes object detection as a direct set prediction problem, establishing an end-to-end framework that circumvents complex post-processing operations such as anchor generation or non-maximum suppression (NMS). By harnessing the self-attention mechanism that has proven highly effective in natural language processing, DETR capably models global relationships between objects within images [8]. However, this approach demonstrates reduced accuracy when detecting smaller objects [11]. YOLO is a one-stage object-detection algorithm where a unified network simultaneously predicts both object locations and classes through a single forward pass of an image. This integrated approach delivers high inference speeds suitable for real-time processing while progressively enhancing the balance between accuracy and efficiency across successive iterations.