# Cascaded Machine-Learning Technique for Debris Classification in Floor-Cleaning Robot Application
## 4 Methodology
### SVM for Error Reduction and Spill Size-Based Classification
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Liquid**} & \multicolumn{4}{c}{**Solid**} & \multicolumn{4}{c}{**Overall**} \\ \cline{2-9}  & **Prec** & **Rec** & \(F_{1}\) & **Prec** & **Rec** & \(F_{1}\) & \(\eta_{miss}\) & \(\eta_{false}\) & **Acc** \\ \hline Faster RCNN ResNet & 96.9 & 99.4 & 98.1 & 98.6 & 93.0 & 95.7 & 3 & 1 & 97.8 \\ Faster RCNN Inception & 91.1 & 97.1 & 94.0 & 93.8 & 82.2 & 87.6 & 7 & 3 & 91.9 \\ Proposed Scheme—SSD MobileNet & 94.2 & 99.3 & 96.7 & 98.5 & 87.8 & 92.8 & 4 & 2 & 95.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of different models for debris detection and classification. \begin{table}
\begin{tabular}{c c} \hline \hline
**Manufacturer and Model** & **Cleaning Path Width (mm)** \\ \hline LG VR66820 VMNC [40] & 195 \\ Samsung POWERbot VR 7000 [41] & 280 \\ iRobot-Roomba 675 [42] & 177 \\ Neato Robotics-Botvac D5 [43] & 304 \\ DeEBOT OZMO 930 [44] & 359 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Inlet Size of some common cleaning robots. ### Comparison with Existing Schemes

This section describes the differences analysis of debris detection and novel-object-detection application using deep-learning technique. Table 5 shows the difference in proposed implementation with existing CNN-based debris-detection and classification schemes. The difference has been listed based on CNN architecture, network hyperparameter, and object-detection accuracy. Faster RCNN architecture has higher accuracy compared to SSD for debris detection and other object-detection scenarios for different feature extractors. However, SSD is faster because it uses a defined set of sizes for the bounding box and uses the more-efficient depthwise convolution layers. These contribute to a lower computation cost but results in a reduction of accuracy. YOLO-based networks are faster than SSD but have a poor accuracy in most tasks. A comparison of the performance of these networks is shown in Table 6. SSD MobileNet provides a good balance between accuracy and computation cost and is fast enough to be used in real-time frameworks for object detection and specifically debris classification. Comparison with non-deep-learning-based approaches is shown in Table 7. Deep-learning techniques offer a large advantage in scenarios when sufficient data is available. These techniques can autonomously extract features from images, which allow them to learn features and patterns which are difficult to figure out statistically. CNN architectures are specifically good for extracting features from images since they use a combination of pixels next to each other for features. Figure 9: Predictions of three networks, Faster RCNN Resnet (**Left**), Faster RCNN Inception (**Center**), Proposed Scheme (**Right**) on the same images. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Scheme** & **Framework** & **Training Dataset Size** & **Epochs** & **mAP (\%)** & **Miss Rate (\%)** & **False Rate (\%)** & **Execution Time (s)** & **Application** \\ \hline [PERSON] et al. [14] & MobileNet SSD & 400 & 10,000 & 95 & - & 2 & 0.12 & Material Surface Defect Detection \\ \hline [PERSON] et al. [13] & CNN (3 layer) & 160 video frames & 1000 & - & - & - & - & Pipe Joint Detection \\ \hline \hline \multirow{3}{*}{[PERSON] et al. [9]} & YOLO v2 & & - & 47.9 & - & - & 0.11 & \\ \cline{2-8}  & tiny YOLO & & - & 31.6 & - & - & 0.52 &
\begin{tabular}{c} Marine \\ Debreis \\ \end{tabular} \\ \cline{2-8}  & Faster RCNN Inception v2 & & - & 81.0 & - & - & 0.97 & Detection \\ \cline{2-8}  & SSD MobileNet & & - & 67.4 & - & - & 3.19 & \\ \hline Proposed & MobileNet SSD & 2000 & 10,000 & 96.4 & 4 & 2 & 0.071 & Debreis Detection and Classification \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison with DL Schemes for Other Applications.