NeRF-Based defect detection
Journal:
arXiv
Published Date:
Mar 31, 2025
Abstract
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.
