## 2 Related Work  
### Object Detection  
Object detection has been considered a fundamental topic in computer vision, and the advancement of deep learning technology has revolutionized this field. Continuous research and development have explored various methodologies and models. Here, we provide a brief overview of key methodologies and models in object detection.  
Traditional Approaches: [PERSON] and [PERSON] (2001) introduced fundamental work on object detection using Haar-like features and a cascaded classifier. This method laid the foundation for real-time face detection and inspired subsequent research [20].  
Region-based CNNs: [PERSON] et al. (2014) introduced the Region-based CNN (R-CNN) [21], which changed the paradigm of object detection by proposing region proposals using selective search for CNN-based classification. Although effective, R-CNN had high computational costs. [PERSON] et al. (2015) improved R-CNN with Fast R-CNN [22], introducing region-of-interest pooling layers for end-to-end training and faster computation. Mask R-CNN [24] performs simultaneous object detection and instance segmentation (masking) in a deep learning-based model. Based on the Faster R-CNN [23] architecture, it not only identifies the location and assigns a class to objects but also precisely segments the object's boundaries at the pixel level to generate masks.  
Single Shot Detectors (SSDs): [PERSON] et al. (2016) proposed the SSD [25], a model predicting bounding boxes and class scores directly from multiple scale feature maps. The SSD achieved real-time object detection performance by passing the network only once.  
You Only Look Once (YOLO): [PERSON] et al. (2016) introduced YOLO [26] as an innovative method for object detection. YOLO divides the input image into a grid and directly predicts bounding boxes and class probabilities, providing an excellent balance between speed and accuracy. It gained notable attention for its real-time performance, and with continuous development since its initial release, version 8 (v8) was launched in 2023. EfficientDet: [PERSON] et al. (2020) proposed EfficientDet [27], a model that balances accuracy and computational efficiency in object detection. It achieved top-level performance across various datasets by introducing a compound scaling method to optimize model parameters.  
DINO (DETR with Improved Denoising Anchor Boxes for End-to-End Object Detection): DINO [28] improves both performance and efficiency relative to earlier DETR-like models. This is achieved through the utilization of a contrastive approach for noise removal training, a blended query selection method for anchor initialization, and two future prediction strategies for box prediction.  
Grounding DINO [29]: This technology performs zero-shot object detection, meaning the model operates on new classes without labeled examples during training. Unlike conventional object detection using trained models on annotated data, Grounding DINO can detect objects even without annotated data for new classes. The core concept of Grounding DINO is the interaction ('grounding') between the visual features of objects and their class names. It utilizes pre-trained DINO to detect objects of new classes and performs interactions by linking the visual features of detected objects with their class names.