## 3 Results and Discussion
### IR Camera  
The experiment was designed such that one IR video could be captured hourly for each pipe. The total experimentation time was 3 h, hence three IR images were produced for a given pipe. Figure 12 presents the IR images collected for the pipes when the moisture content of the sand was approximately 2%. The RGA was used to process the IR images by utilizing the image histogram to identify the coolest pixel in the image and use it as the seed pixel. Then, the neighboring pixels of the initial seed pixel were examined to determine if they can be added to the region. In this study, the three images taken for every pipe type over a three-hour period were processed using the coolest pixel in each image (i.e., the pixel with the lowest temperature). Then, the region-growing algorithm was implemented on  
Figure 11: GPR images obtained on PPR, PVC and PE setup with 10% soil moisture content.  
each image using image variance as the threshold criteria for accepting or rejecting pixels. Then, the resulting regions in the three images were compared. Leak are identified only for the resulting regions that were increasing with time. If the three seed pixels are at the same location, a unique leak location is identified. Otherwise, the locations of the three seed pixels are potential locations of the leakage. The RGA was implemented in ArcGIS 6.5 using the Reclassifier tool in the Classification Wizard. The essence of using the spectrometer was to identify the wavelength(s) that are sensitive to the identification of the leaks through moisture so that an appropriate imaging device with these identified wavelength(s) can be used in the future. The criteria used for the detection of leaks in the spectrometer considers any reflectance/transmittance of less than 50% in wavelengths larger than 0.70 \(\upmu\)m as an indication. Figure 13 below demonstrates the RGA method by showing the location of the seed pixel in a sample image and the region created using the RGA criteria mentioned above. Note that in Figure 13c, the number of pixels that compose the region created the surroundings of the seed pixel. It is evident that the IR camera was able to robustly detect leaks in all pipe materials (PE, PPR and PVC) for all of the tested leak types (crack, hole and faulty joint) when the soil moisture content was 2%. Hot spot analysis of the IR images has been employed to detect the leaks. As observed from the IR images in Figure 12, there was a formation of blue spots in the IR image with time. These blue spots are locations where the temperature was reduced. This temperature reduction can be explained because of the increase in the moisture content of the sand surrounding the leak. Hence, as the time of the leak increases, the amount of water leaking to the surrounding sand increases, thereby, increasing the blue spots in the IR image. Similarly, Figure 14 shows the IR images obtained when the moisture content of the soil was approximately 5%. The IR camera was successful in detecting leaks in the pipes when the moisture content was 5%. Contrary to this, the IR camera was unable to detect the leaks when the moisture content of the soil was approximately 10%, as shown in Figure 15. Additionally, unlike the GPR, the IR camera could be utilized on pipe 4 (faulty joint) as the images are captured from a distance, safeguarding the camera from the water stagnated on the surface of pipe 4. The increase in the percentage of soil moisture reduces the ability of the IR camera to distinguish between the temperature contrasts between the soil and leaking water. However, IR images with high resolution may be able to capture this.  
Figure 12: IR images obtained on PPR, PVC and PE setup with 2% soil moisture content.  
Figure 14: IR images obtained on PPR, PVC and PE setup with 5% soil moisture content.  
Figure 13: (**a**) Sample IR image; (**b**) image histogram; and (**c**) identified leakage locations.