## 3 Methodology  
In this work, NFD for estimating near-front distance using a monocular camera on public objects is proposed. The solution can be utilized as a navigation aid for visually impaired people. Figure 1 depicts the architecture of NFD, which is a three-tier cloud solution that consists of a smartphone with a built-in monocular camera, a computer for image processing, and a cloud computing platform for model training. Images are first captured by the smartphone and transferred to the computer for the creation of the dataset. Once the dataset is ready, it is sent to the cloud computing platform for neural network training and inference. After training, a model will be outputted for distance estimation. Training, calibration, and testing are the three processes that compose NFD specifically. The specifics are as follows:  
Training. In the first stage, NFD establishes a deep learning model for training to detect public objects (e.g., fences in the street), which is detailed in [15]. During the training, a certain amount of street view images of public objects were taken by smartphone cameras at random poses. Those images were imported into a computer for the dataset pre-processing, which includes image selection, format converting, and resizing by image editing software. An annotation software, LabelImg v1.8.6 [32], was then used for labeling the positions and classes of target objects manually. The exported \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Existing Solution** & **Technique/Model** & **Hardware** & **Target Object** & **Advantages** & **Disadvantages** \\ \hline [PERSON] and [PERSON] [26] & 3D point cloud & 3D ToF camera \& tablet & Structural Object (e.g., doorway, hallway, stairway, ground, and wall) & Highly accurate and possible to combine with SLAM/wayfinding solutions & High computing resources required and indoor only \\ [PERSON] et al. [27] & YOLOv3-tiny & Smartphone (built-in RGB camera and infrared depth camera) & Human & High mobility and off-the-shelf device required & Very specific application and short distance \\ [PERSON] et al. [28] & YOLOv3 & Stereo webcam \& NVIDIA Jetson TX2 & Indoor furniture (e.g., chair and table) & Low cost but small dataset required & Low mobility and accuracy in distance estimation \\ [PERSON] and [PERSON] [29] & AlexNet & Webcam \& laptop outdoor buildings & Indoor objects and outdoor buildings & Tenton involved, available in both in indoor and outdoor & Only workable in the authors’ campus and low mobility \\ [PERSON] et al. [30] & MobileNet-SSDv2 & Micro-controllers, Raspberry PiCam \& webcam, ultrasonic \& webcam, ultrasonic \& infrared (VOC and COCO dataset) & High mobility, available in low power, and low cost & Additional sensors for distance estimation required. Implemented with the existing navigation tool \\ [PERSON] et al. [31] & Ultrasonic echolocation & Speaker \& ultrasonic microphones & Any object in short distance & Work without visible light, provide 3D spatial information & Short distance and could not recognize objects \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between NFD and existing solutions. annotation files were grouped with the pre-processed images and uploaded to the GPU-enabled cloud computing platform, Google Colaboratory [33], for the training process. The Darknet [34], an open-source neural network framework specialized for YOLO [35], was set up on the cloud and different versions of YOLO were executed on it for training the model. Finally, trained weights files were outputted and saved on the network drive. Multiple sessions of training were performed to seek the most suitable configurations and the best weights before the testing. Calibration. In the second stage, when a new camera is used, calibration must be performed to acquire the camera's intrinsic parameters. During the calibration stage, the camera was set up at fixed poses with known heights and pitch angles.