Machine learning shadowgraph for particle size and shape characterization
Abstract
Conventional image processing for a particle shadow image is usually time-consuming and suffers degraded image segmentation when dealing with images consisting of complex-shaped and clustered particles with varying backgrounds. In this paper, we introduce a robust learning-based method using a single convolution neural network for analyzing particle shadow images. Our approach employs a two-channel-output U-net model to generate a binary particle image and a particle centroid image. The binary particle image is subsequently segmented through a marker-controlled watershed approach with the particle centroid image as the marker image. The assessment of this method on both synthetic and experimental bubble images has exhibited a better performance compared to the state-of-art non-machine-learning method. The proposed machine learning shadow image processing approach provides a promising tool for real-time particle image analysis in industrial applications.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 28, 2020
- Source ID
- 10.1088/1361-6501/abae90
Entities
People
- Jiaqi Li
- Jiarong Hong
- Siyao Shao
Organizations
- Office of Naval Research