## 3 Results
### The Effect of Approach Distance on Detection Scores  
Table 15 shows the result of real-time detection scores based on the distance variation of 10 cm from 35 cm to 65 cm for images with aspect ratio 416 \(\times\) 416 with Enhance 3.5. The results showed that the system could still detect all the workpieces at a 45 cm approach distance. However, when the distance is at 65 cm, two thirds of the workpieces are undetected and unrecognized, marked as \"-\".  
Figure 10 shows a boxplot summarizing the enhanced dataset. The detection scores for a distance of 45 cm have the smallest variation while those of a distance of 65 cm have the largest variation with the undetected workpiece. The figure also shows that distances of 35 cm and 55 cm have few outliers. Therefore, an approach distance of 45 cm is the most suitable approach distance for the robot to obtain the best detection scores.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Enhancement Level** & **Classes** & **35 cm (\%)** & **45 cm (\%)** & **55 cm (\%)** & **65 cm (\%)** \\ \hline \multirow{8}{*}{1.5} & Blue-cube & 94 & 98 & 96 & 81 \\  & Blue-cyl & 97 & 97 & 93 & 56 \\  & Red-cube & 95 & 96 & 78 & - \\  & Red-cyl & 93 & 97 & 93 & 90 \\  & Yellow-cube & 58 & 93 & 90 & 93 \\  & Yellow-cyl & 99 & 93 & 89 & 97 \\ \hline \multirow{8}{*}{2.5} & Blue-cube & 98 & 97 & 95 & 80 \\  & Blue-cyl & 93 & 98 & 78 & - \\  & Red-cube & 95 & 91 & 86 & - \\  & Red-cyl & 85 & 92 & 75 & 51 \\  & Yellow-cube & 64 & 97 & 50 & 51 \\  & Yellow-cyl & 94 & 76 & 78 & 65 \\ \hline \multirow{8}{*}{3.5} & Blue-cube & 96 & 96 & 88 & 70 \\  & Blue-cyl & 89 & 94 & 95 & - \\ \cline{1-1}  & Red-cube & 96 & 89 & 88 & - \\ \cline{1-1}  & Red-cyl & 94 & 96 & 93 & 71 \\ \cline{1-1}  & Yellow-cube & 76 & 84 & 90 & 79 \\ \cline{1-1}  & Yellow-cyl & 97 & 86 & 91 & 85 \\ \hline \multicolumn{2}{c}{Average detection scores} & 89.61 & 92.78 & 85.89 & 74.54 \\ \hline \multicolumn{2}{c}{Standard deviation} & 11.43 & 5.68 & 10.78 & 14.84 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Detection scores with variation of approach distance.  
Figure 9: Comparison of inference with quantization.  
To enhance our results, we have added signal processing for the detection scores of an approach distance of 45 cm, as shown in Table 16. We used the Savgol filter [34] to remove the outliers to the detection scores. According to [PERSON] et. al. [35], the Savgol filter removes the effect of the outliers but preserves the signal tendency.  
We use the basic Savgol filter with a window of 5 and a polynomial degree of 2 from the python SciPy library [36], which is a Python library used for scientific computing and technical computing. As shown in Figure 11, the graph has become smoother after we removed the outliers such as the low detection scores of RGB enhanced level 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{tabular}{c} **Enhancement** \\ **Level** \\ \end{tabular} & \multirow{2}{*}{**Classes**} & \
\begin{tabular}{c} **Original Data Scores** \\ **with Outliers (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Processed Data Using** \\ **Savgol Filter (\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{1.5} & Blue-cube & 98 & 97.63 \\  & Blue-cyl & 97 & 97.49 \\  & Red-cube & 96 & 96.77 \\  & Red-cyl & 97 & 95.63 \\  & Yellow-cube & 93 & 93.77 \\  & Yellow-cyl & 93 & 93.6 \\ \hline \multirow{4}{*}{2.5} & Blue-cube & 97 & 96.83 \\  & Blue-cyl & 98 & 96.2 \\  & Red-cube & 91 & 92.71 \\  & Red-cyl & 92 & 94.23 \\  & Yellow-cube & 97 & 88.69 \\  & Yellow-cyl & 76 & 87.14 \\ \hline \multirow{4}{*}{3.5} & Blue-cube & 96 & 88.97 \\  & Blue-cyl & 94 & 94.34 \\ \cline{1-1}  & Red-cube & 89 & 92.94 \\ \cline{1-1}  & Red-cyl & 96 & 90.51 \\ \cline{1-1}  & Yellow-cube & 84 & 88.06 \\ \cline{1-1}  & Yellow-cyl & 86 & 84.89 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Detection scores with optimum approach distance of 45 cm.  
Figure 10: Boxplot of detection scores.