A Novel Approach for UAV Image Crack Detection
Cracks are the most significant pre-disaster of a road, and are also important indicators for evaluating the damage level of a road. At present, road crack detection mainly depends on manual detection and road detection vehicles, with which the safety of detection workers is not guaranteed and the d...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI
2022
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| Subjects: | |
| Online Access: | View Fulltext in Publisher |
| LEADER | 02461nam a2200445Ia 4500 | ||
|---|---|---|---|
| 001 | 10.3390-s22093305 | ||
| 008 | 220510s2022 CNT 000 0 und d | ||
| 020 | |a 14248220 (ISSN) | ||
| 245 | 1 | 0 | |a A Novel Approach for UAV Image Crack Detection |
| 260 | 0 | |b MDPI |c 2022 | |
| 856 | |z View Fulltext in Publisher |u https://doi.org/10.3390/s22093305 | ||
| 520 | 3 | |a Cracks are the most significant pre-disaster of a road, and are also important indicators for evaluating the damage level of a road. At present, road crack detection mainly depends on manual detection and road detection vehicles, with which the safety of detection workers is not guaranteed and the detection efficiency is low. A road detection vehicle can speed up the efficiency to a certain extent, but the automation level is low and it is easy to block the traffic. Unmanned Aerial Vehicles (UAV) have the characteristics of low energy consumption and easy control. If UAV technology can be applied to road crack detection, it will greatly improve the detection efficiency and produce huge economic benefits. In order to find a way to apply UAV to road crack detection, we developed a new technique for road crack detection based on UAV pictures, called DenxiDeepCrack, which is a trainable deep convolutional neural network for automatic crack detection which utilises learning high-level features for crack representation. In addition, we create a new dataset based on drone images called UCrack 11 to enrich the crack database of drone images for future crack detection research. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. | |
| 650 | 0 | 4 | |a Aircraft detection |
| 650 | 0 | 4 | |a Antennas |
| 650 | 0 | 4 | |a Convolutional neural networks |
| 650 | 0 | 4 | |a crack detection |
| 650 | 0 | 4 | |a Crack detection |
| 650 | 0 | 4 | |a Damage level |
| 650 | 0 | 4 | |a deeep learning |
| 650 | 0 | 4 | |a Deeep learning |
| 650 | 0 | 4 | |a Deep neural networks |
| 650 | 0 | 4 | |a Detection efficiency |
| 650 | 0 | 4 | |a Drones |
| 650 | 0 | 4 | |a Energy utilization |
| 650 | 0 | 4 | |a image stitching |
| 650 | 0 | 4 | |a Image stitching |
| 650 | 0 | 4 | |a Road cracks |
| 650 | 0 | 4 | |a Road detection |
| 650 | 0 | 4 | |a Roads and streets |
| 650 | 0 | 4 | |a Speed up |
| 650 | 0 | 4 | |a target detection |
| 650 | 0 | 4 | |a Targets detection |
| 650 | 0 | 4 | |a unmanned aerial vehicle |
| 650 | 0 | 4 | |a Vehicle images |
| 650 | 0 | 4 | |a Workers' |
| 700 | 1 | |a Li, Y. |e author | |
| 700 | 1 | |a Ma, J. |e author | |
| 700 | 1 | |a Shi, G. |e author | |
| 700 | 1 | |a Zhao, Z. |e author | |
| 773 | |t Sensors | ||