Irregularity Inspection using Neural Radiance Field
This addresses safety and efficiency issues for industries relying on manual inspections of tall machinery, though it appears incremental as it applies an existing method (NeRF) to a new domain.
The paper tackles the problem of defect detection in large-scale industrial machinery by proposing a system based on neural radiance fields (NeRF) to create 3D twin models, enabling automated and precise defect detection through comparison of digital models.
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.