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This paper provides a comprehensive review of the application of computer vision technology in the automatic detection of road potholes and objective assessment of road conditions. Typically, cameras and various types of depth sensors are used to acquire two-dimensional and three-dimensional road data for road imaging. Pothole detection is carried out based on computer vision technology, with the main detection algorithms including classic two-dimensional image processing, three-dimensional point cloud modeling and segmentation, deep learning, and their hybrid methods. The hybrid methods, which exploit the advantages of various algorithms, can greatly improve the accuracy of detection. However, while existing algorithms have achieved good results in pothole detection, they still face many challenges, such as the need to improve the robustness of road geometry reconstruction, high algorithm complexity, and the model’s strong dependence on large-scale well-annotated datasets. Therefore, future research should focus more on unsupervised stereo matching algorithms and deep learning algorithms with few samples.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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