Irregularity Inspection using Neural Radiance Field

TL;DR

This paper introduces a neural radiance field-based system for defect detection in large-scale industrial machinery by comparing 3D models, enhancing automation, safety, and inspection precision.

Contribution

It proposes a novel neural radiance field approach for 3D defect detection, addressing challenges of traditional visual inspections on tall machinery.

Findings

Effective defect detection through 3D model comparison

Improved safety by reducing manual inspections

Enhanced accuracy over traditional visual methods

Abstract

With the increasing growth of industrialization, more and more industries are relying on machine automation for production. However, defect detection in large-scale production machinery is becoming increasingly important. Due to their large size and height, it is often challenging for professionals to conduct defect inspections on such large machinery. For example, the inspection of aging and misalignment of components on tall machinery like towers requires companies to assign dedicated personnel. Employees need to climb the towers and either visually inspect or take photos to detect safety hazards in these large machines. Direct visual inspection is limited by its low level of automation, lack of precision, and safety concerns associated with personnel climbing the towers. Therefore, in this paper, we propose a system based on neural network modeling (NeRF) of 3D twin models. By comparing two digital models, this system enables defect detection at the 3D interface of an object.