## 2 Related Works
### Object Detection Method  
The existing object detection method is separate from traditional detection techniques such as the Histogram of Oriented Gradients (HOG) [15] and Deformable Parts Model (DPM) [16] algorithms and deep learning-based detection techniques. The HOG is an algorithm that detects formulaic objects by converting the regional gradient direction information of an image into a histogram to express its shape. It has the advantage of strong noise, as it performs calculations in units of cells rather than pixels. The DPM is an algorithm that detects an object in a block of a bounding box using a template filter. As the operation is performed through the sliding window, many bounding boxes are generated, and the detection process is complicated. Along with the mentioned algorithms, traditional detection techniques have the disadvantage of having limited applications that can be applied and fewer classes that can be classified. In contrast, deep learning-based detection techniques are combined with deep learning algorithms to overcome the shortcomings of traditional detection techniques. As a result, this method is applicable to various applications and showed good performance in classifying many classes. Deep learning object detection algorithms are divided into two types. One is a two-stage method, in which regional proposal and classification are processed sequentially. These methods include R-CNN [17], Fast R-CNN [18], and Faster R-CNN [19] as representative algorithms. R-CNN is a model that uses a CNN, which classifies objects in a two-stage method, and a regional proposal algorithm to locate an object's bounding boxes, and it uses a linear support vector machine (SVM) [20] to extract features and classify them into classes based on CNN operations. However, the disadvantage is that it takes a lot of inference time to carry out CNN operations on all the regions of interest (ROIs) obtained using the regional proposal algorithm. Faster R-CNN was proposed to compensate for the shortcomings that result from the long inference times of R-CNN. Since the selective search is computed outside the CNN, bottlenecks occur at the ROI generation stage. Faster R-CNN has eliminated the selective search while inheriting the structure of the existing Fast R-CNN. It is an object detection algorithm that improves on the slow speed by using a region proposal network (RPN) that infers ROIs from feature maps and an ROI pooling layer that generates fixed-size feature vectors. Figure 1 shows the architecture of Faster R-CNN. Figure 1: Architecture of Faster R-CNN. The other method is a one-stage method, in which regional proposal and classification are processed simultaneously. This involves the YOLO series of algorithms and the SSD algorithm. The YOLO algorithm is used to predict the class and location of an object in an image. This algorithm is also suitable for detecting objects in real time, with a faster computational speed compared with Faster R-CNN. The YOLO algorithm proposes a new approach to incorporating a structure and finding objects by predicting the bounding boxes in an image, and it has a simplified pipeline and fewer computations, enabling inference speeds fast enough for real-time systems while using contextual information to reduce background errors. However, the initial YOLO model has the disadvantage of not detecting small objects or predicting multiple objects as a group due to spatial constraints. It also suffers from localization errors. In the next version of the model, YOLOv2, using anchor boxes to predict objects is proposed to improve the low recall performance. This allows the YOLOv2 model to achieve a significant increase in recall performance. This is because k-means clustering and a sigmoid activation function are applied along with the anchor boxes. In addition, the size of the input image can be varied while removing fully connected layers in the YOLOv2 model. Thus, the YOLOv2 model can balance speed and accuracy for the purpose of the task.