Synthetic Data for Defect Segmentation on Complex Metal Surfaces
Author:
Affiliation:
1. Fraunhofer ITWM,Kaiserslautern,67663
2. RPTU Kaiserslautern-Landau,Kaiserslautern,67663
Funder
Ministry of Education
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10208270/10208119/10208376.pdf?arnumber=10208376
Reference59 articles.
1. Digital reality: a model-based approach to supervised learning from synthetic data
2. Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection
3. Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY
4. Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges
5. Automatic Defects Segmentation and Identification by Deep Learning Algorithm with Pulsed Thermography: Synthetic and Experimental Data
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