An active learning framework featured Monte Carlo dropout strategy for deep learning-based semantic segmentation of concrete cracks from images
| Author(s): |
Chow Jun Kang
(Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, HKSAR, China)
Wong Cho Hin Peter (Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, HKSAR, China) Tan Pin Siang (Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, HKSAR, China) Tan Tun Jian (Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, HKSAR, China) Li Zhaofeng (Shenzhen Key Laboratory of Intelligent Structure System in Civil Engineering, Harbin Institute of Technology, Shenzhen, China) Wang Yu-Hsing 
(Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, HKSAR, China)
|
|---|---|
| Medium: | journal article |
| Language(s): | English |
| Published in: | Structural Health Monitoring, February 2023, n. 5, v. 22 |
| Page(s): | 147592172211503 |
| DOI: | 10.1177/14759217221150376 |
- About this
data sheet - Reference-ID
10714754 - Published on:
21/03/2023 - Last updated on:
01/09/2023
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