Autonomous crack inspection on GNSS-denied areas using convolutional neural networks and ultra-wideband positioning system
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ID: 286940
2023
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Abstract
Modern inspections on concrete pavements and buildings are done using unmanned aerial vehicles (UAV) and machine vision to detect cracks remotely. While drones are efficient in inspections, they mostly rely on global navigation satellite system (GNSS) for positioning. UAV missions on GNSS-denied areas use either manual or semi-automatic pathing due to the lack of a positioning system. This study proposes the use of an ultrawide-band positioning system as an alternative to control and locate the drone for autonomous flight. When combined with convolutional neural networks, it would be able to detect and approximate the location of the crack. Tests were done using the alternative positioning system and classification and segmentation network to detect and locate the various cracks placed on a rectangular test wall of different sizes. The results show that the inspection system was able to autonomously navigate around the test areas and collect images of the wall along with its cracks. The system was able to detect and segment the cracks with 94.85% accuracy and approximate its location through a visualization of the crack location within the test area. This gives proof that ultrawide-band and convolutional neural networks could be used as a solution for autonomous inspection on GNSS-denied areas. If developed and improved, it would be possible to use this for regular autonomous inspection on important structures without satellite coverage.
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| Authors | Sorilla, Joses S. |
| Journal | Malay Journal |
| Year | 2023 |
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| Keywords | Keywords not found |
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