# Mask R-CNN with New Data Augmentation Features for Smart Detection of Retail Products
## 3 Experimental Results
### D. Results and Comparison
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Items** & **YOLO v3** & \begin{tabular}{c} **Faster** \\ **R-CNN [4]** \\ \end{tabular} & \begin{tabular}{c} **Mask** \\ **R-CNN [8]** \\ \end{tabular} & \begin{tabular}{c} **Mask** \\ **R-CNN + Aug.** \\ \end{tabular} & \
\begin{tabular}{c} **Mask** \\ **R-CNN + Aug. +** \\ **DWT (LL)** \\ \end{tabular} &
\begin{tabular}{c} **Mask** \\ **R-CNN + Aug. +** \\ **DWT (LL + HH)** \\ \end{tabular} \\ \hline CocoCola & 0.99 & 0.98 & 0.99 & 0.99 & 0.99 & 0.99 \\ \hline Cashewnuts & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline M\&M large & 0.94 & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline M\&M small & 0.97 & 0.99 & 0.98 & 0.99 & 0.99 & 0.99 \\ \hline Hot chili squid flavor & 0.97 & 0.96 & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline Original flavor & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline Thai spicy mixed nuts & 0.94 & 0.99 & 0.99 & 1.00 & 1.00 & 1.00 \\ \hline Pringles & 1.00 & 1.00 & 1.00 & 0.99 & 0.99 & 1.00 \\ \hline Taro fish snack & 0.82 & 0.89 & 0.94 & 0.91 & 0.95 & 0.95 \\ \hline Salted peanuts & 0.94 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline Mandarin & 1.00 & 1.00 & 1.00 & 0.99 & 0.99 & 1.00 \\ \hline Spearmint & 0.93 & 0.88 & 0.94 & 0.97 & 0.96 & 0.98 \\ \hline Fried seaweed & 1.00 & 0.98 & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline Pon de strawberry honey & 0.96 & 1.00 & 1.00 & 0.96 & 1.00 & 1.00 \\ \hline Pon de strawberry & 0.96 & 0.95 & 0.95 & 1.00 & 1.00 & 1.00 \\ \hline Pon de double chocolate & 0.90 & 0.91 & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline Pon de chocolate & 0.80 & 0.98 & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline Pon de soybean & 0.77 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline Pon de yogurt & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline Chocolate old fashioned & 0.80 & 0.74 & 0.85 & 0.92 & 0.96 & 1.00 \\ \hline Old fashioned & 0.81 & 0.92 & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline Strawberry ring & 1.00 & 0.92 & 0.84 & 1.00 & 1.00 & 1.00 \\ \hline Sugar ring & 0.87 & 0.93 & 0.86 & 0.86 & 0.92 & 0.92 \\ \hline ACT (ms) & \textless{}100 & 1900 & 194.10 & 192.29 & 220.07 & 194.47 \\ \hline mAP (\%) & 92.89 & 95.71 & 97.09 & 98.26 & 98.95 & **99.27** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of APs using different algorithms for each item. left column shows the detection result for a faster R-CNN [4] and the right column shows the detection result for the proposed algorithm: Mask R-CNN with DA and DWT. In sets 1 through 6, the red cross signifies the original algorithm's failure to recognize objects and the red arrow signifies its X failure to detect objects. The proposed algorithm recognizes and detects all objects. The experimental results show that the proposed technique could be effectively utilized to perform smart stores; using the Snacks database, this study achieved a high detection rate. In the Snacks database, there are a total of 3108 objects, and this work can detect 3099 objects correctly, thus only 9 objects have false detections. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Faster R-CNN** & **Set** & **This Work** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of results.