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Early and timely defect detection is essential to ensure the safe operation of hydraulic concrete structures. The deep learning-based computer vision method does not require complex manual feature engineering, and can automatically determine the category of structural defects in remote images, overcoming the shortcomings of traditional manual vision that are labor-intensive, subjective and prone to errors. Inspired by this, this paper proposes a deep learning-based defect detection method, which introduces attention mechanism into the ResNeXt50 network to adaptively recalibrate the channel-level feature responses, so that the model can pay more attention to the defect information in the image and enhance the feature extraction ability. Test results show that the proposed method can achieve an F1 score of 88.0%, and realize a good classification effect for common concrete defects.
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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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