FPFH 기반 기하학적 특징 증강을 이용한 조선소 3D 딥러닝 데이터셋 경량화 및 객체 탐지 성능 분석

Abstract

As digital twin technology for implementing smart shipyards advances, research on object detection using 3D point cloud has become increasingly necessary. Shipyards, in particular, are complex environments with coexisting indoor and outdoor settings, large structures, and diverse equipment, making the analysis of features from 3D point cloud objects essential for effective object detection. However, these environments, characterized by intricate geometries, varied equipment scales, and high-resolution data, generate massive datasets. Such large volumes of data lead to challenges in storage, transmission, processing, and learning resources and costs, notably intensifying computational and memory loads during the training of deep learning-based object detection models. Therefore, a lightweighting solution that ensures efficient processing while maintaining data quality is required. Accordingly, this study extracted geometric local features using Fast Point Feature Histograms (FPFH) from 3D point cloud acquired in a shipyard. An augmented dataset was then constructed based on these extracted feature points. Subsequently, an object detection model was trained using a 3D point cloud-based deep learning architecture to analyze the impact of these features on object detection accuracy. The results indicated that the FPFH-applied dataset maintained comparable accuracy to the original dataset. This research is anticipated to be valuably utilized for the development of automation technologies and digital twins aimed at constructing smart shipyards.