## 5 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Method**} & \multicolumn{1}{c}{**Using Device**} & \multicolumn{1}{c}{**Result**} & \multicolumn{1}{c}{**Connection**} & \multicolumn{1}{c}{**Cost**} \\ \hline \multirow{3}{*}{[PERSON] et al. [10]} & Single board, neural computer stick2, stereo camera, SMA actuator, vibration motors, and signal amplifier circuit & Detect obstacles and vibration pattern output & Offline & High \\ \hline \multirow{3}{*}{[PERSON] et al. [11]} & Depth camera on smart glass, laptop, and headphone & Detect obstacles and generate warning clarinet sound & Online & High \\ \hline \multirow{3}{*}{[PERSON] et al. [12]} & Camera, smartwatch, and smartphone & Detect obstacles and generate audio output & Online & High \\ \hline \multirow{3}{*}{[PERSON] et al. [13]} & RGB-D camera and IMU sensors & Detect obstacles and generate audio output & Online & High \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Proposed Method \\ \end{tabular} } & \multirow{3}{*}{Webcam, single board, and headphone} & Detect obstacles and generate walking information audio output & Online & Low (70 USD) \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison with related works. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Number of Class**} & \multicolumn{1}{c}{\begin{tabular}{c} **Number of** \\ **Objects in Frame** \\ \end{tabular} } & \multicolumn{1}{c}{\begin{tabular}{c} **Average Time Taken for** \\ **Object Detection (s)** \\ \end{tabular} } & \multicolumn{1}{c}{\begin{tabular}{c} **Average Time Taken for** \\ **Sentence Generation (s)** \\ \end{tabular} } & \multicolumn{1}{c}{\
\begin{tabular}{c} **Average Time of** \\ **Audio Prompt (s)** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **Total Time to Process** \\ **a Single Frame (s)** \\ \end{tabular} } \\ \hline \multirow{3}{*}{\(1\)} & \multirow{3}{*}{\(1\)} & \(1\) & 0.30 (0.01) & 1.90 (0.01) & 0.50 (0.01) & 2.70 (0.01) \\  & & & 0.0929 & 0.5344 & 0.7922 & 0.2463 \\ \hline \multirow{3}{*}{\(1\)} & \multirow{3}{*}{\(p\)-value} & 4 & 0.30 (0.01) & 1.90 (0.01) & 0.50 (0.01) & 2.70 (0.02) \\  & & & 0.9056 & 0.7922 & 0.1560 & 0.8398 \\ \hline \multirow{3}{*}{\(2\)} & \multirow{3}{*}{\(p\)-value} & 2 & 0.31 (0.01) & 2.11 (0.02) & 0.61 (0.02) & 3.02 (0.02) \\  & & & 0.8352 & 0.9827 & 0.9194 & 0.8974 \\ \hline \multirow{3}{*}{\(2\)} & \multirow{3}{*}{\(p\)-value} & 5 & 0.30 (0.01) & 2.11 (0.01) & 0.51 (0.01) & 2.91 (0.02) \\  & & & 0.8352 & 0.9116 & 0.9116 & 0.8705 \\ \hline \multirow{3}{*}{\(3\)} & \multirow{3}{*}{\(p\)-value} & 3 & 0.30 (0.01) & 2.11 (0.02) & 0.51 (0.01) & 2.91 (0.01) \\  & & & 0.9056 & 0.8733 & 0.9651 & 0.9380 \\ \hline \multirow{3}{*}{\(3\)} & \multirow{3}{*}{\(p\)-value} & 4 & 0.30 (0.01) & 2.20 (0.01) & 0.70 (0.01) & 3.20 (0.01) \\  & & & 0.0555 & 0.4082 & 0.4082 & 0.1973 \\ \hline \multirow{3}{*}{\(3\)} & \multirow{3}{*}{\(p\)-value} & 7 & 0.30 (0.01) & 2.10 (0.01) & 0.60 (0.01) & 3.00 (0.01) \\  & & & 0.0555 & 0.7922 & 0.0270 \({}^{\star}\) & 0.4082 \\ \hline \multirow{3}{*}{\(4\)} & \multirow{3}{*}{\(p\)-value} & 10 & 0.31 (0.01) & 2.20 (0.01) & 0.50 (0.01) & 3.00 (0.01) \\  & & & 0.5224 & 0.4082 & 0.7922 & 0.5573 \\ \hline \multirow{3}{*}{\(5\)} & \multirow{3}{*}{\(p\)-value} & 12 & 0.31 (0.01) & 2.10 (0.01) & 0.70 (0.01) & 3.20 (0.01) \\  & & & 0.5224 & 0.3657 & 0.4082 & 0.0672 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Processing time of each frame in different conditions in a single board computer. Numbers in parentheses indicate the standard deviations. The Shapiro-Wilk test was used as a statistical method to verify the normality of each result data. The asterisk (*) indicates statistical significance at \(p\) < 0.05. A qualitative evaluation of the proposed system was conducted through a pilot test with one visually impaired person and three blindfolded healthy people in their 20s.