| Abstract [eng] |
This work reviews various methods for visualizing underground pipe defects and contamination. The review encompasses techniques such as CCTV cameras, convolutional neural networks, stereo vision, acoustic reflection, and ultrasonic visualization analyses. The described methods are based on distance measurement using laser reflection angle, pulsed ToF, and continuous wave amplitude modulated (AMCW) ToF. A small-sized ToF sensor is used in the study, allowing for visualizations of underground pipe contamination with artificially created contamination simulations. The paper describes studies examining four types of contamination simulations: mud deposits, deposits around the pipe perimeter, tree roots, and fat deposits. The capabilities of the ToF sensor to visualize blockages in underground pipes were tested by varying the distance between the simulated contamination and the sensor, and by changing the sensor’s zone sharpness parameter. An optimization study for contamination visualization was conducted. Additionally, the evaluation of images obtained with the ToF sensor in 8x8 and 16x16 resolutions was performed by merging four 8x8 resolution images. Analysis of the obtained simulation images revealed that the sensor successfully visualizes contamination simulations up to 30 cm away from the sensor. It was proven that zone sharpness is an important parameter for achieving the most accurate visualization. Furthermore, the visualization quality study results showed that the average visualization error for 8x8 resolution images is 6.27%, while for 16x16 resolution images it is 2.29%. |
