Segmenting Unseen Industrial Components in a Heavy Clutter Using RGB-D Fusion and Synthetic Data

Autor: Seungjun Choi, JongWon Kim, Kyoobin Lee, Seunghyeok Back, Raeyoung Kang
Rok vydání: 2020
Předmět:
FOS: Computer and information sciences
0209 industrial biotechnology
Computer science
business.industry
Computer Vision and Pattern Recognition (cs.CV)
Image and Video Processing (eess.IV)
Computer Science - Computer Vision and Pattern Recognition
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Pattern recognition
02 engineering and technology
Electrical Engineering and Systems Science - Image and Video Processing
Pipeline (software)
Synthetic data
020901 industrial engineering & automation
Feature (computer vision)
0202 electrical engineering
electronic engineering
information engineering
FOS: Electrical engineering
electronic engineering
information engineering
RGB color model
Clutter
020201 artificial intelligence & image processing
Segmentation
Artificial intelligence
business
Focus (optics)
Zdroj: ICIP
DOI: 10.48550/arxiv.2002.03501
Popis: Segmentation of unseen industrial parts is essential for autonomous industrial systems. However, industrial components are texture-less, reflective, and often found in cluttered and unstructured environments with heavy occlusion, which makes it more challenging to deal with unseen objects. To tackle this problem, we present a synthetic data generation pipeline that randomizes textures via domain randomization to focus on the shape information. In addition, we propose an RGB-D Fusion Mask R-CNN with a confidence map estimator, which exploits reliable depth information in multiple feature levels. We transferred the trained model to real-world scenarios and evaluated its performance by making comparisons with baselines and ablation studies. We demonstrate that our methods, which use only synthetic data, could be effective solutions for unseen industrial components segmentation.
Comment: 5 pages,6 figures, Accepted to ICIP 2020
Databáze: OpenAIRE
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