## 5 Experimental Results  
The proposed system was implemented on a single board to detect situations and objects that may occur in the walking environments of visually impaired individuals using a webcam. It automatically generates obstacle notifications and caution sentences based on the names of detected objects. The proposed system integrates object recognition and natural language generation through the Slack API [56], a cloud-based messaging platform. When the number of frames in which YOLOv5 recognizes an object reaches a certain threshold, the object's name is sent to the Slack user channel. Subsequently, the object name sent to the user channel is retrieved using the \"conversations.history\" API method, which searches existing messages in the Slack channel and is passed to the KoAlpaca generator. The notification and cautionary sentences generated based on the transmitted object name are then sent back to the Slack channel via the \"chat.postMessage\" API. Finally, the sent sentences are converted to speech via a TTS module, which is delivered to the user through headphones connected to the single board. The single board used was a Jetson Orin Nano, an embedded computing board from NVIDIA designed for lightweight, low-power systems. Unlike existing single boards, such as Arduino and Raspberry Pi, it was designed to support AI computations. The implementation on a single board was based on NVIDIA's Ampere GPU architecture [57], using NVIDIA's CUDA as the GPU development tool. The system was developed using Anaconda Python(3.7.0), and PyTorch(11.3) software. Table 10 lists the environment of the computing system in the Jetson Orin Nano. A time domain analysis was conducted to verify the applicability of the proposed system. The analysis examined the number of classes and objects within a frame, the average time taken for object detection and sentence generation, the average time of audio prompts, and the total time to process a single frame to verify the performance across various scenarios. Table 11 presents the results of the time domain analysis of the proposed system. We used the Shapiro-Wilk test method to verify the normality of the resulting data \begin{table}
\begin{tabular}{c c} \hline \hline
**Computing Source** & **Version** \\ \hline CPU & 6-core Arm Cortex-A78 EA v8.2 \\ RAM & 8 GB \\ GPU & 1024-core NVIDIA Ampere architecture GPU with 32 Tensor Cores \\ Anaconda Python Ver. & 3.7.0 \\ CUDA Engine Ver. & CUDA 11.3 \\ Pytorch Ver. & 11.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The environment of the computing source in Jetson Orin Nano. Figure 10: Loss evaluation value of KoAlpaca. of the four processing indices. As a result, except for the average time of the audio prompt of class 3 and seven objects in the frame (_p_-value = 0.0270), it was confirmed that all of them followed a normal distribution through normality verification. The proposed system automatically detects objects and situations and generates cautionary sentences based on them. This allows for conveying contextually understood sentences about complex situations rather than object information. Additionally, by delivering sentences via a TTS module, the system can reduce its reliance on sensory-dependent aids, such as canes and clickers, which visually impaired individuals commonly use. Compared with other methods that use various sensors and devices for object detection and information conveyance to support the visually impaired, the proposed system achieves detection with a low-cost webcam and conveys cautionary sentences through headphones. Table 12 presents the results of comparing the proposed system with related works in this field.