# Mask R-CNN-Based Stone Detection and Segmentation for Underground Pipeline Exploration Robots
## 4 Conclusions  
In this research study, Mask-RCNN was applied to detect stones inside an underground pipeline through an inspection robot. Stones are a very common object that can be found on pipeline surfaces during a regular inspection period. Since natural sunlight does not reach into an underground pipe, artificial lights with moderate intensity are used during the inspection time. Furthermore, because the stones vary in size and color, it is difficult to accurately identify the stones using a straightforward image processing technique. To detect the stones in moderate light, it is crucial to understand the background and the stones. Through a simple image processing approach, it is hard to contend with background noise. Mask R-CNN can reliably detect stones and understand the background of the pipeline, even at low or medium brightness. All the images were collected from inside a pipe with moderate lighting and a variety of backgrounds and stones. The total number of images was 280, which covers various angles and contains approximately 3500 stones. The test results of our stone detection model showed that the precision, recall, and F1 scores were 92.0%, 90.0%, and 91.0%, respectively. Distance measurement algorithms can be used in real time to calculate the distance between a robot and each stone with an error margin of 11.36 mm. The idea of this project can be useful for researchers in this field for any kind of underground pipeline exploration work. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{Trail Images Detail} & \multicolumn{4}{c}{Measurement Unit: mm} \\ \hline Test Images & No. of Objects & Serial No. & Actual Distance & Measured Distance & Absolute Error \\ \hline \multirow{4}{*}{Figure 11d} & & 1 & 328.50 & 315.75 & 12.75 \\  & 3 & 2 & 447.00 & 430.00 & 17.00 \\  & & 3 & 635.00 & 622.00 & 13.00 \\ \hline \multirow{4}{*}{Figure 11e} & & 1 & 262.00 & 248.50 & 13.50 \\  & & 2 & 268.50 & 254.75 & 13.75 \\  & & 3 & 335.00 & 314.25 & 20.75 \\  & 6 & 4 & 317.00 & 328.75 & 11.75 \\  & & 5 & 379.00 & 398.75 & 19.75 \\  & & 6 & 453.00 & 442.75 & 10.25 \\ \hline \multirow{4}{*}{Figure 11f} & & 1 & 260.50 & 243.75 & 16.75 \\  & & 2 & 267.00 & 253.25 & 13.75 \\  & & 3 & 280.50 & 273.75 & 6.75 \\  & & 4 & 318.00 & 305.25 & 12.75 \\  & 8 & 5 & 320.50 & 312.75 & 7.75 \\  & & 6 & 313.00 & 324.75 & 11.75 \\  & & 7 & 402.00 & 389.00 & 13.00 \\  & & 8 & 441.00 & 433.25 & 7.75 \\ \hline \multirow{4}{*}{Figure 11g} & & 1 & 242.50 & 230.75 & 11.75 \\  & & 2 & 318.00 & 310.50 & 7.50 \\  & 4 & 3 & 357.00 & 342.50 & 14.50 \\  & & 4 & 410.00 & 393.75 & 16.25 \\ \hline \multirow{4}{*}{Figure 11h} & & 1 & 278.00 & 265.75 & 12.25 \\  & & 2 & 309.00 & 314.75 & 5.75 \\  & & 3 & 345.00 & 335.50 & 9.50 \\  & & 4 & 373.50 & 361.25 & 12.25 \\  & & 5 & 431.00 & 418.50 & 12.50 \\  & & 6 & 507.00 & 492.75 & 14.25 \\ \hline Total & 40 & & & 454.25 \\ \hline \multicolumn{3}{c}{Average absolute error} & & & 11.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cont. **Author Contributions**: Conceptualization, H.K. and H.-S.L. : Methodology, H.K. and H.-S.L. ; Formal analysis, H.K. ; Investigation, H.K. ; Resources, H.-S.L. ; Data curation, H.K. : Writing--original draft, H.K. ; Writing--review & editing, H.-S.L. ; Supervision, H.-S.L. ; Funding acquisition, H.-S.L. All authors have read and agreed to the published version of the manuscript. **Funding**: This paper was supported by the Joongbu University Research and Development Fund in 2022. **Institutional Review Board Statement**: Not applicable. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Mask R-CNN. _IEEE Trans. Pattern Anal. Mach. Intell._**2020**, _42_, 386-397. [CrossRef] [PubMedMed]
* [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON]; [PERSON] Faster R-CNN: Towards real-time object detection with region proposal networks. _arXiv_**2015**, arXiv:1506.01497v3. [CrossRef] [PubMedMed]
* [PERSON] et al.