用NeRF和无人机构建设备3D模型，自动检测缺陷。
NeRF-Based defect detection
- 通过无人机拍摄重建设备3D模型，生成标准与当前状态对比
- 采用ICP算法对齐点云，精准识别形变等异常偏差
- 替代人工巡检，提升安全性和可扩展性，适合大型工业场景
工业自动化快速发展，对大规模机械设备的精确高效缺陷检测需求日益迫切。传统检查依赖人工攀爬高处目视评估，耗时耗力、主观性强且存在安全隐患。本文提出一种基于神经辐射场（NeRF）与数字孪生概念的自动化缺陷检测框架。系统利用无人机采集图像，重建设备3D模型，生成标准参考模型与实时状态模型进行对比。通过迭代最近点（ICP）算法实现模型对齐，进而开展精确点云分析，识别出表征潜在缺陷的偏差。该方法摆脱了人工巡检，显著提升检测精度与作业安全性，并为工业应用提供可扩展的解决方案。实验表明，该方法在可靠性和效率方面具有广阔应用前景。
原文摘要 · Abstract (English)
The rapid growth of industrial automation has highlighted the need for precise and efficient defect detection in large-scale machinery. Traditional inspection techniques, involving manual procedures such as scaling tall structures for visual evaluation, are labor-intensive, subjective, and often hazardous. To overcome these challenges, this paper introduces an automated defect detection framework built on Neural Radiance Fields (NeRF) and the concept of digital twins. The system utilizes UAVs to capture images and reconstruct 3D models of machinery, producing both a standard reference model and a current-state model for comparison. Alignment of the models is achieved through the Iterative Closest Point (ICP) algorithm, enabling precise point cloud analysis to detect deviations that signify potential defects. By eliminating manual inspection, this method improves accuracy, enhances operational safety, and offers a scalable solution for defect detection. The proposed approach demonstrates great promise for reliable and efficient industrial applications.
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