| 李扬涛,赵海涛,顾冲时,魏洋,包腾飞,向镇洋.基于无人机图像和深度学习的水工混凝土结构三维重建和缺陷定量辨识方法[J].水利学报,2025,56(9):1155-1165 |
| 基于无人机图像和深度学习的水工混凝土结构三维重建和缺陷定量辨识方法 |
| Three-dimensional reconstruction and defect quantitative identification method of hydraulic concrete structure based on UAV images and deep learning |
| 投稿时间：2024-09-29 修订日期：2025-06-11 |
| DOI：10.13243/j.cnki.slxb.20240627 |
| 中文关键词: 水工混凝土结构 缺陷识别 无人机航拍 机器视觉 人工智能 |
| 英文关键词: hydraulic concrete structure defect identification drone aerial photography machine vision artificial intelligence |
| 基金项目:西藏自治区科技计划项目（XZ202501ZY0009）；国家杰出青年科学基金项目（52325803）；国家自然科学基金项目（52378244）；江苏省自然科学基金项目（BK20231293）；江苏省重点研发计划项目（BE2020703）；国家基金委区域创新联合基金重点项目（U22A20229） |
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| 中文摘要: |
| 水工混凝土结构服役期内易出现裂缝、钙质物析出等缺陷，传统人工巡检手段存在检测效率低、潜在风险大、误判率高等劣势，难以在高耸结构区域实施。据此，本文提出一种基于多视图立体视觉和深度学习的水工混凝土结构三维重建和缺陷定量辨识方法。以U-Net3+模型为基础，引入坐标注意力机制模块，提升对微小缺陷区域的聚焦能力和复杂背景干扰场景缺陷分割精度；提出基于无人机图像的混凝土结构三维重建方法，构建视觉三维重建逆向反演框架，开展逆向工程计算并还原成像过程，将二维图像中的缺陷与三维实景模型相结合，建立基于多视图立体视觉的混凝土结构缺陷定位与量化方法。以混凝土梁和某混凝土高坝为例，验证所提方法的三维重建精度、缺陷精细辨识和定位量化效果。结果表明，本文提出方法可实现水工混凝土结构缺陷的精确定量辨识和空间定位，有效提高检测效率，为缺陷长期追踪监测提供技术支撑。 |
| 英文摘要: |
| During the service life of concrete structures，they are prone to defects such as cracks，spalling，and calcium precipitation due to environmental erosion and multi-load coupling. Traditional manual inspection methods suffer from drawbacks，including low detection efficiency，high potential risks，and a high rate of misjudgment， making them difficult to implement in high-rise structural areas. To address these challenges，this paper proposes a method for 3D reconstruction and defect quantification of concrete structures using multi-view stereo vision and deep learning. Building on the U-Net3+ model，the paper introduces the coordinate attention mechanism and a classification guidance module to enhance the model's ability to focus on minute defect areas and improve the accuracy of defect segmentation in complex，background-interfered environments. Recognizing that traditional machine vision detection based on 2D images struggles with defect localization and accurate size quantification，a 3D reconstruction method for concrete structures from drone images is developed. A visual 3D reconstruction framework is established to perform reverse engineering calculations and restore the imaging process. Defects identified in 2D images are integrated with the 3D real-world scene model，and a method for defect localization and quantification in concrete structures using multi-view stereo vision is proposed. Concrete beams and a concrete high dam are used as engineering case studies to validate the accuracy of the proposed 3D reconstruction for concrete structures and the effectiveness of fine defect identification and localization. The results demonstrate that the method proposed in this paper enables precise quantitative identification and spatial localization of defects in concrete structures，even under the complex background interference of UAV-based aerial imagery. This approach significantly improves detection efficiency and provides valuable technical support for long-term defect tracking and monitoring. |
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