## 2 Literature Review
### YOLO Model-Based Detection Methods  
First, we reviewed works on implementations of the _You Only Look Once (YOLO)_ model in indoor navigation systems. It integrates a camera to detect specific objects in the environment. _YOLO_ determines the user's position or minimizes navigation errors based on the detected objects according to the defined parameters of the indoor navigation system. In [21], [PERSON] et al. presented a modified _YOLOv1_-based neural network for object detection to improve detection accuracy and speed. The proposed model enhances the original _YOLOv1_ by adjusting the loss function to a proportion style, adding a spatial pyramid pooling layer, and incorporating an inception model with a \(1\times 1\) convolution kernel to reduce weight parameters. Extensive experiments on the Pascal VOC 2007/2012 datasets demonstrated that the modified model achieved better performance than the original _YOLOv1_. In [22], [PERSON] et al. developed a fine-tuned model of _YOLOv2_ to extract vehicle-type information from images or videos. The model incorporates k-means++ clustering for vehicle bounding boxes, normalization for bounding box dimensions, and a multi-layer feature fusion strategy. The fine-tuned model achieved a _mAP_ of 94.78% on the BIT-Vehicle validation dataset and demonstrated the ability to predict new datasets without encountering them in the training process. However, due to the low variety and amount of vehicle-type data, incorporating an additional dataset could further improve the model's accuracy and robustness. In [23], [PERSON] proposed a new method for initializing the width and height of predicted bounding boxes to improve the _YOLOv3_ object detection model. The method uses Markov chains and intersection-over-union for faster convergence and more accurate initial cluster centers. On the MS COCO dataset, it achieves an average _IoU_ of 60.44% (0.56% higher) and a running time \(1/297\) of the original, while on the PASCAL VOC dataset, it achieves an average _IoU_ of 67.45% (0.13% higher) and a running time \(1/81\) of the original. The proposed method outperforms _YOLOv3_ in terms of recall, _mAP_, F1-score, and the detection of small objects. In [24], [PERSON] et al. proposed a _YPD-SLAM_ system that provides a real-time _VSLAM_ in dynamic indoor environments. It integrates _YOLO-FastestV2_ target detection and _Cylinder and Plane extraction (CAPE)_. _YOLO-FastestV2_ detects and isolates the target to enhance the feature point extraction. The deployed proposed system only utilizes CPU for cost-effective hardware implementation. It achieved robust, accurate, and real-time performance. However, tracking failures were detected when the user rotated too quickly. In [25], [PERSON] et al. proposed a _Visual Simultaneous Localization and Mapping (VSLAM)_ algorithm that is based on _ORB-SLAM3_ for dynamic scenarios. The proposed algorithm is integrated with the _YOLOv5 object detection_ model. It utilizes the depth information to categorize detected objects and eliminate dynamic feature points. The results indicate an improvement in accuracy compared to the original _ORB-SLAM3_. This highlights the effectiveness of the algorithm in dynamic scenarios. However, some feature points are removed in stationary scenarios, and missed object class detection is observed. In [26], [PERSON] et al. proposed a transfer-learning-based model for real-time object detection, enhancing the _YOLOv6_ algorithm through pruning and finetuning to improve detection accuracy and inference speed. The model, which utilizes Google Text-to-Speech for audio feedback, was trained on the MS-COCO dataset and demonstrates significant improvements over various baseline models, achieving a 37.8% higher average precision with 1235 frames per second. Despite its improved performance, the model struggles with detecting objects against textured backgrounds. In [27], [PERSON] proposed _Indoor Human Detection (IHD)_ for drones using the _YOLO-IHD_ model.