*Geographic Information Systems in Geospatial Intelligence*

**6. Conclusions**

**Table 7.**

**Defect ID#**

> **Length (cm)**

**Acknowledgements**

support.

**169**

RGB + depth data for improved pothole detection.

**Ground-truth Proposed**

**Width (cm)**

*DOI: http://dx.doi.org/10.5772/intechopen.88877*

**method**

**Width (cm)**

*On the Use of Low-Cost RGB-D Sensors for Autonomous Pothole Detection with Spatial…*

*Sample comparison of detected pothole metrological parameters with ground-truth measurements.*

**Length (%)**

1 53.5 48.8 52.2 45.4 2.43 7.00 4.4 21.38 94.072 6 26.1 17.8 29.1 13.9 11.49 28.26 5.6 27.21 152.376 11 64.4 60.1 60.9 63.4 5.43 5.49 3.8 18.46 70.148 27 45.9 47.7 42.0 46.3 8.50 2.94 .59 28.66 169.094

**Width (%)**

**Length (cm)**

of the new data and their processing challenges and results.

This chapter presents a robust approach for cost-effect detection of potholes on asphalt pavements. By first proposing a system for pavement surface mapping using Kinect v2.o and based on the iMMSS hardware-software system, the implementation first incorporates *k*-means clustering and horizontal-vertical integration as data search or filtering algorithms, followed with spatial fuzzy *c*-means (SPCM) segmentation for pothole and non-pothole detection. The results of the processing illustrates the potential of using RGB and depth image in the detection of potholes based on low-cost consumer grade sensors, and shows the potential of fusing

**Relative error Proposed iMMSS method**

**Mean depth (cm)**

**Mean area (cm<sup>2</sup> )** **Volume (cm<sup>3</sup> )**

From the experimental analysis, it is conclusive that using a single Kinect may not only limit the maximum traveling speed for data collection, but does not also cover the whole width of a traffic lane. This means that the field of view (FOV) can be increased by determining and using an array of Kinect sensors so that the lateral data collection extent can be increased. Further, the development of suitable depth and RGB fusion should be investigated both at object and at feature fusion levels. In summary, it is demonstrated that low-cost and high-performance vision and depth sensors are capable of providing new possibilities for achieving autonomous inspection of pavement structures, and are suitable for overcoming the spatial and temporal limitations associated with both the manual human-based inspection and the expensive techniques. Overall, the findings of the study are significant, in terms

This research work was carried with the framework of research sponsorship by the Alexander von Humboldt Foundation (Germany), and the author would like to acknowledge and thank the Alexander von Humboldt Foundation for the financial

#### **Figure 9.**

*Illustration of the significance of depth in pothole edge mapping in relation to pothole data fusion and improved detection. (i) RGB image. (ii) Depth map.*


#### **Table 5.**

*Confusion matrix of the evaluated pothole-defect detection system.*


#### **Table 6.**

*Overall performance of the pothole-defect detection system.*

*On the Use of Low-Cost RGB-D Sensors for Autonomous Pothole Detection with Spatial… DOI: http://dx.doi.org/10.5772/intechopen.88877*


**Table 7.**

**Figure 9.**

**Table 5.**

**Table 6.**

**168**

*detection. (i) RGB image. (ii) Depth map.*

*Illustration of the significance of depth in pothole edge mapping in relation to pothole data fusion and improved*

**Classified Defective Defect-free** Defective (potholes) TP = 40 FP = 5 Defect-free (non-potholes) FN = 15 TN = 50

**Index Accuracy (%) Precision (%) Recall (%)** Value 82.8 88.8 72.7

**Prediction Ground truth**

*Confusion matrix of the evaluated pothole-defect detection system.*

*Geographic Information Systems in Geospatial Intelligence*

*Overall performance of the pothole-defect detection system.*

*Sample comparison of detected pothole metrological parameters with ground-truth measurements.*
