# Cascaded Machine-Learning Technique for Debris Classification in Floor-Cleaning Robot Application
## 4 Methodology
### SVM for Error Reduction and Spill Size-Based Classification
Then, the captured images are sent to CNN architecture for debris classification. Finally, the detected liquid spillage debris region is evaluated by an SVM to classify the liquid spillage debris based on size into large and small regions. Figure 8 shows the SVM classification results for various size of liquid spillage debris. A Violet bounding box indicates large liquid spillage debris and a red bounding box indicates small liquid spillage debris. Figure 5: Front View Results. Figure 6: Detection of mixed debris. The experiment results show the efficacy of the proposed scheme for recognition and classification of commonly found debris in a home/food court environment. The SVM classifier achieved 91.6% classification accuracy. For large liquid spillage debris, the SVM classifier achieved 0.99 precision, 0.81 recall and an F-measure score of 0.89. For small liquid spillage debris, the model achieved 0.88 precision, 0.99 recall and an F-measure score of 0.94. The overall time taken by the SVM for an image on an average is 0.01 s.

For solid debris, the threshold is determined according to the size of vacuuming inlet of the cleaning device. The standard inlet size of commercial floor-cleaning robots is reported in Table 2. According to vacuuming inlet dimension, we can fix the threshold for solid objects to determine whether the detected objects should be avoided or cleaned. Furthermore, larger (in height) solid debris higher than ground clearance height (the gap between robot base and ground) of the robot is

Figure 8: Spill Size Classification Results. Turquoise box indicates CNN spill detection, Violet box indicates large spillage debris and red indicates small spillage debris. All areas are in cm\({}^{2}\). (**a**) Area: 1167.4; (**b**) Area: 1461.2; (**c**) Area: 888.8; (**d**) Area: 75.4; (**e**) Area: 158.4; (**f**) Area: 28.3, 111.3, 54.9; (**g**) Area: 89.1. Figure 7: Camera arrangement for capturing floor images in the experiments. (**a**) Robot Perspective; (**b**) Top Perspective. considered to be an obstacle which can be detected by the bump sensor present in the robot and is automatically excluded from cleaning. ### Comparison of Performance of Different Architectures

To assess the efficiency of our proposed scheme, the classification accuracy of the proposed scheme has been compared with more robust networks such as Faster RCNN ResNet and Faster RCNN Inception CNN architectures. Both networks have been trained using the same dataset for a similar amount of time. The metrics indicate that Faster RCNN ResNet performs the best compared to the other model. Faster RCNN Inception generally performs better compared to SSD MobileNet but not in the scenario of debris type classification. In contrast to the other schemes, SSD MobileNet uses a significantly lesser amount of time for each image. Also, SSD MobileNet is optimized for low powered hardware which makes it possible to deploy the network in real-time situations using low powered computing hardware such as Intel Movidius Neural Compute Stick and Raspberry Pi. Hence, the proposed scheme can be implemented on a small cleaning robot for real-time debris detection and classification. The garbage detection and classification results for Faster RCNN Resnet, Faster RCNN Inception and the proposed scheme are shown in Table 3. The comparison is made using standard metrics discussed above. Also, timing analysis has been performed to estimate the average detection time of each network. It has been tested on NVIDIA Quadro P4000 graphic card and reported in Table 4. The comparison of predictions results of three schemes on two given images is shown in Figure 6.0