Visual Deformation Detection Using Soft Material Simulation for Pre-training of Condition Assessment Models

Autor:	Sol, Joel, Enayati, Amir M. Soufi, Najjaran, Homayoun
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Graphics Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	This paper addresses the challenge of geometric quality assurance in manufacturing, particularly when human assessment is required. It proposes using Blender, an open-source simulation tool, to create synthetic datasets for machine learning (ML) models. The process involves translating expert information into shape key parameters to simulate deformations, generating images for both deformed and non-deformed objects. The study explores the impact of discrepancies between real and simulated environments on ML model performance and investigates the effect of different simulation backgrounds on model sensitivity. Additionally, the study aims to enhance the model's robustness to camera positioning by generating datasets with a variety of randomized viewpoints. The entire process, from data synthesis to model training and testing, is implemented using a Python API interfacing with Blender. An experiment with a soda can object validates the accuracy of the proposed pipeline. Comment: 6 pages, 4 figures, submitted to CASE
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.14877 Zobrazit plný text záznamu View this record from Arxiv