Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Survey

TL;DR

This survey reviews recent deep learning advancements in processing 3D point clouds for industrial defect detection and classification, emphasizing shape segmentation and the challenges faced in applying neural networks to industrial condition monitoring.

Contribution

It provides a comprehensive overview of DL-based methods for 3D PC defect classification and segmentation, highlighting strengths, limitations, and future directions in industrial applications.

Findings

Deep learning effectively improves defect detection accuracy.

Current methods face challenges in shape segmentation and real-time processing.

Insights into enhancing condition monitoring and remaining useful life estimation.

Abstract

In recent years, 3D point clouds (PCs) have gained significant attention due to their diverse applications across various fields, such as computer vision (CV), condition monitoring (CM), virtual reality, robotics, autonomous driving, etc. Deep learning (DL) has proven effective in leveraging 3D PCs to address various challenges encountered in 2D vision. However, applying deep neural networks (DNNs) to process 3D PCs presents unique challenges. This paper provides an in-depth review of recent advancements in DL-based industrial CM using 3D PCs, with a specific focus on defect shape classification and segmentation within industrial applications. Recognizing the crucial role of these aspects in industrial maintenance, the paper offers insightful observations on the strengths and limitations of the reviewed DL-based PC processing methods. This knowledge synthesis aims to contribute to understanding and enhancing CM processes, particularly within the framework of remaining useful life (RUL), in industrial systems.