基于圆结构光视觉的管道缺陷识别及三维重构方法研究的中期报告.docx

基于圆结构光视觉的管道缺陷识别及三维重构方法研究的中期报告 摘要： 本文基于圆结构光视觉技术，提出一种管道缺陷识别及三维重构方法。该方法利用圆形结构光视觉技术获取管道表面的三维模型，然后通过图像处理和机器学习等技术，对管道表面的缺陷进行识别，并将识别结果映射到三维模型上，实现对管道缺陷的三维重构和可视化。 本文首先介绍了圆结构光视觉技术的原理及其在三维重构中的应用。然后针对管道表面缺陷识别问题，提出了一种基于深度学习的分类方法，并对数据集进行了采集和处理。最后，在实际的数据集上进行了实验验证，并对实验结果进行了分析和讨论。 实验结果表明，本文提出的管道缺陷识别及三维重构方法具有较高的准确率和鲁棒性。该方法可以为工业管道的检测和维护提供有效的技术手段。 关键词：圆结构光视觉；管道缺陷识别；三维重构；深度学习 Abstract: In this paper, a pipeline defect recognition and 3D reconstruction method based on circular structured light vision technology is proposed. This method uses circular structured light vision technology to obtain a three-dimensional model of the pipeline surface, and then uses image processing and machine learning technologies to recognize surface defects of the pipeline, and maps the recognition results to the three-dimensional model to achieve 3D reconstruction and visualization of pipeline defects. This paper first introduces the principle of circular structured light vision technology and its application in 3D reconstruction. Then, a classification method based on deep learning is proposed for the pipeline surface defect recognition problem, and the data set is collected and processed. Finally, experiments were conducted on actual data sets, and the experimental results were analyzed and discussed. The experimental results show that the pipeline defect recognition and 3D reconstruction method proposed in this paper has high accuracy and robustness. This method can provide an effective technical means for the detection and maintenance of industrial pipelines. Keywords: Circular structured light vision; Pipeline defect recognition; 3D reconstruction; Deep learning