# 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} \hline \hline
**Scheme** & **Framework** & **Training Dataset Size** & **Batch Size** & **Decay** & **Initial Learning Rate** & **mAP (\%)** & **Execution Time (s)** \\ \hline [PERSON] et al. [27] & AlexNet & 2451 & 100 & 0.0005 & 0.01 & 87.69 & 1.50 \\ [PERSON] et al. [19] & CNN (11 layer) & 2400 & 32 & 0.5 & 0.5 & 22.0 & - \\ [PERSON] et al. [28] & OverFeat GoogleNet & 18,676 & 16 & - & - & 63.2 & - \\ [PERSON] et al. [10] & Fast RCNN & 1999 & 32 & 0.0005 & - & - & 0.22 \\ Proposed & MobileNet SSD & 2000 & 32 & 0.9 & 0.002 & 96.4 & 0.071 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with Similar Schemes. ## 6 Conclusions and Future Work

This work proposed debris detection and liquid spillage debris classification based on size for floor-cleaning robot application using cascaded machine-learning algorithms involving SSD Mobile net deep-learning framework and SVM classifier. The feasibility of the proposed method was verified with real debris images captured with the top perspective and the robot perspective. The experimental results show that the proposed scheme can identify and classify both solid and liquid spillage debris with more than 96% accuracy in both perspectives and achieves better detection and classification performance than existing deep-learning schemes and other computer vision-based debris classification techniques. Furthermore, for deploying in the real floor-cleaning robotic platform, the robot perspective has added advantages than top perspective. Therefore, in most of the vision system-enabled floor-cleaning devices, the vision modules are fixed in robot perspective and can cover the large floor area (approximately 400 cm coverage area from current position). These factors make the robot perspective a better approach for spill detection, which can be easily deployed in any specific existing or new platform. As the proposed technique has the potential to be implemented in real-world applications, we plan to deploy this scheme in reconfigurable robots being developed at SUTD, Singapore [45; 46]. Furthermore, we plan to bridge the vSLAM algorithm with the proposed scheme to resolve the occlusion issues in real-time implementation on floor-cleaning robots. Through vSLAM techniques, robots can compute the debris location and update the debris location in vision-based mapping systems, which helps track the detected object even if it is occluded by another object. [PERSON], [PERSON], [PERSON] and [PERSON] designed the algorithm, and carried out the experiment, analyzed the result and wrote the paper; [PERSON] and [PERSON] analyzed the data and gave helpful suggestion on this research. This work is financially supported by the National Robotics R&D Program Office, Singapore, under the Grant No. RGAST1702. The authors declare no conflict of interest. ## References

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