PDF
Full
Outline
Crack detection is crucial to maintaining the structural safety of buildings. In recent years, convolutional neural networks based on deep learning have provided new solutions for crack detection. However, this comes at the cost of huge computing resources, so there are problems of poor real-time performance and low detection efficiency in practical applications. To address this problem, a lightweight MSFC (multi-scale dynamic fusion convolution module) based on the U-Net architecture was proposed to improve the efficiency of crack segmentation. To verify the effectiveness of the proposed method, a dataset Crack2045 containing 2 045 crack images was constructed and experiments were conducted on this dataset. The experimental results show that compared with the original U-Net model, the model using the MSFC module reduces 78.51% of the parameters and 63.75% of the computational complexity while maintaining the same accuracy. At the same time, the MSFC module has a certain degree of generalization and can be seamlessly integrated into different semantic segmentation models. This study not only provides an efficient deep learning method for crack detection, but also provides new possibilities for model deployment in resource-constrained environments.
PDF
257
117
| 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 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62199257
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
