2023, 15(5): 52-57. doi: 10.16670/j.cnki.cn11-5823/tu.2023.05.09
基于深度学习的桥梁病害检测研究与应用
| 1. | 桥梁智能与绿色建造全国重点实验室，武汉 430034 |
| 2. | 中铁大桥科学研究院有限公司，武汉 430034 |
| 3. | 高速铁路建造技术国家工程研究中心，长沙 410075 |
Research and Application of Bridge Disease Detection Based on Deep Learning
| 1. | National Key Laboratory of Bridge Intelligent and Green Construction, Wuhan 430034, China |
| 2. | China Railway Bridge Research Institute Co., Ltd., Wuhan 430034, China |
| 3. | National Engineering Research Center of High-Speed Railway Construction Technology, Changsha 410075, China |
引用本文:
陶世峰, 吴巨峰, 周强, 赵训刚, 王熊珏, 谈遂. 基于深度学习的桥梁病害检测研究与应用[J]. 土木建筑工程信息技术,
2023, 15(5): 52-57.
doi: 10.16670/j.cnki.cn11-5823/tu.2023.05.09
Citation:
Shifeng Tao, Jufeng Wu, Qiang Zhou, Xungang Zhao, Xiongjue Wang, Sui Tan. Research and Application of Bridge Disease Detection Based on Deep Learning[J]. Journal of Information Technologyin Civil Engineering and Architecture,
2023, 15(5): 52-57.
doi: 10.16670/j.cnki.cn11-5823/tu.2023.05.09
摘要:本文研发基于YOLOv5算法的桥梁病害检测系统，用于无人机桥梁病害检测。首先，通过无人机巡航系统进行病害采集；其次，通过K-means聚类算法对YOLOv5算法中的先验框比例进行改进，获取桥梁病害的尺寸；再次，利用图像切割算法将无人机输入图像切割为640*640尺寸大小，减小模型训练与推理难度，同时利用APAP算法对切割图像识别结果进行拼接，达到完整无人机图像识别结果；最后，采用Python Flask搭建开放式桥梁病害检测Web前端，可根据实际需求调用不同桥梁部位病害权重，实现13种桥梁病害实时检测。结果表明，该模型可有效地降低硬件需求，可直接对无人机图像进行识别处理，实现快速病害检测，有望为桥梁病害检测提供一个高效、安全且具有前景的检测方法。
Abstract: In this paper, a bridge disease detection system based on YOLOv5 algorithm is proposed for UAV bridge disease detection. Firstly, diseases are collected through UAV cruise system. Secondly, K-means clustering algorithm is applied to improve the prior box proportion in YOLOv5 algorithm to obtain the size of bridge disease. Thirdly, the UAV input image is cut to 640 * 640 size by using the image cutting algorithm to reduce the difficulty of model training and reasoning. What's more, this paper uses APAP algorithm to splice the recognition results of cutting images to achieve a complete UAV image recognition result. Finally, the paper employs Python Flask to build an open bridge disease detection Web front-end, which can call the disease weights of different bridge parts according to actual needs, and further realize real-time detection of 13 bridge diseases. The results show that the model can effectively reduce the hardware requirements, directly identify and process UAV images, and achieve rapid disease detection, which is expected to provide an efficient, safe and promising detection method for bridge disease detection.
| [1] |
Yongding Tian, Chao Chen, Kwesi Sagoe-Crentsil, et al. Intelligent robotic systems for structural health monitoring: Applications and future trends[J]. Automation in Construction, 2022, 139(7): 104273. |
| [2] |
Tangbo Bai, Jianwei Yang, Guiyang Xu, et al. An optimized railway fastener detection method based on modified Faster R-CNN[J]. Measurement, 2021, 182(9): 109742. |
| [3] |
Yongqing Jiang, Dandan Pang, Chengdong Li. A deep learning approach for fast detection and classification of concrete damage[J]. Automation in Construction, 2021, 128(8): 103785. |
| [4] |
Chong Wei, Shurong Li, Kai Wu, et al. Damage inspection for road markings based on images with hierarchical semantic segmentation strategy and dynamic homography estimation[J]. Automation in Construction, 2021, 131(11): 103876. |
| [5] |
Srinath S. Kumar, Dulcy M. Abraham, Mohammad R. Jahanshahi, et al. Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks[J]. Automation in Construction, 2018, 91(7): 273-283. |
| [6] |
杨扬, 王连发, 张宇峰, 等. 基于多特征融合的混凝土结构表面病害图像分类算法[J]. 长安大学学报: 自然科学版, 2021, 41(3): 64-74. |
| [7] |
Xiaojian Han, Zhicheng Zhao, Lingkun Chen, et al. Structural damage-causing concrete cracking detection based on a deep-learning method[J]. Construction and Building Materials, 2022, 337(24): 127562 |
| [8] |
陈飞飞, 张宇峰, 韩晓健. 基于图像特征值的混凝土桥梁表面病害图像分类[J]. 结构工程师, 2018, 34(1): 59-63. |
| [9] |
杨魁, 王丹妮, 唐双, 等. 基于改进YOLO算法的混凝土表观病害识别方法[J]. 公路工程, 2021, 46(5): 81-86. |
| [10] |
Cha Y J, Choi W, Suh G, et al. Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types[J]. Computer-Aided Civil and Infrastructure Engineering, 2018, 33(9): 731-747.doi: 10.1111/mice.12334 |
| [11] |
Yu Z, Shen Y, Shen C. A real-time detection approach for bridge cracks based on YOLOv4-FPM[J]. Automation in Construction, 2021, 122(2): 103514. |
| [12] |
Zhang, C, Chang, C-C, Jamshidi, M. Concrete bridge surface damage detection using a single – stage detector[J]. Computer -Aided Civil and Infrastructure Engineering, 2020, 122(2): 103514. |
| [13] |
丁威, 俞珂, 舒江鹏. 基于深度学习和无人机的混凝土结构裂缝检测方法[J]. 土木工程学报, 2021, 54(S01): 1-12. |
| [14] |
阮小丽, 王波, 荆国强, 等. 桥梁混凝土结构表面裂缝自动识别技术研究[J]. 世界桥梁, 2017, 45(6): 55-59. |
| [15] |
R. Santos, D. Ribeiro, P. Lopes et al. Detection of exposed steel rebars based on deep-learning techniques and unmanned aerial vehicles[J]. Automation in Construction, 2022, 139(7): 104324. |
| [16] |
阮小丽, 王波, 吴巨峰, 等. 基于深度学习的钢筋混凝土桥梁掉块露筋病害识别[J]. 世界桥梁, 2020, 48(6): 88-92. |
| [17] |
Isaac Osei Agyemang, Xiaoling Zhang, Daniel Acheampong, et al. Agbley, Autonomous health assessment of civil infrastructure using deep learning and smart devices[J]. Automation in Construction, 2022, 141(9): 104396. |
| [18] |
Yan Xu, Jian Zhang. UAV-based bridge geometric shape measurement using automatic bridge component detection and distributed multi-view reconstruction[J]. Automation in Construction, 2022, 140(8): 104376. |
计量
- PDF下载量(23)
- 文章访问量(659)
- HTML全文浏览量(509)
