In:
tm - Technisches Messen, Walter de Gruyter GmbH, Vol. 90, No. 7-8 ( 2023-07-27), p. 500-511
Abstract:
This contribution proposes an approach and the respective tool that uses Active Deep Learning (ADL) to segment industrial three-dimensional computed tomography (3D CT) data. The general approach is application-independent and includes an iterative human-in-the-loop Active Learning (AL) process that produces labeled training data and a trained Deep Learning (DL) model for semantic segmentation. The model is continuously improved during iterations such that manual labeling effort is reduced. In addition, the user can minimize user interaction with the aid of a random forest-based classifier and focus on unclear or invalid segmentation results. The complete workflow is implemented within one single Python tool. The approach is demonstrated in detail for two industrial use cases: Single fiber analysis and plant segmentation. For plant segmentation, the method is compared to a baseline and a classic image processing algorithm.
Type of Medium:
Online Resource
ISSN:
0171-8096
,
2196-7113
DOI:
10.1515/teme-2023-0047
Language:
English
Publisher:
Walter de Gruyter GmbH
Publication Date:
2023
detail.hit.zdb_id:
2025790-9
SSG:
15,3
Bookmarklink