Target Domain Training Data Generation by Moving Object Detection and Label Propagation-Reference-Cited by-同舟云学术

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Target Domain Training Data Generation by Moving Object Detection and Label Propagation

Published:2024-01-25 Issue: Volume: Page:380-387
ISSN:
Container-title:Computing in Civil Engineering 2023
language:
Short-container-title:

Author:

Kim Taegeon¹²³⁴⁵,Shin Giwon¹²³⁴⁵,Kim Seokhwan¹²³⁴⁵,Asari Vijayan K.¹²³⁴⁵,Kim Hongjo¹²³⁴⁵

Affiliation:

1. Ph.D. Student, Dept. of Civil and Environmental Engineering, Yonsei Univ., Seoul, Korea.

2. Undergraduate Student, Dept. of Civil and Environmental Engineering, Yonsei Univ., Seoul, Korea.

3. Ph.D Student, Dept. of Civil and Environmental Engineering, Yonsei Univ., Seoul, Korea.

4. Professor, Dept. of Electrical and Computer Engineering, Univ. of Dayton, OH.

5. Assistant Professor, Dept. of Civil and Environmental Engineering, Yonsei Univ., Seoul, Korea.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485224.046

Reference18 articles.

1. Image augmentation to improve construction resource detection using generative adversarial networks, cut-and-paste, and image transformation techniques

2. Bewley A. Z. Ge L. Ott F. Ramos and B. Upcroft. (2016). “Simple online and realtime tracking.” 2016 IEEE Int. Conf. Image Process. ICIP 3464–3468.

3. Vision-based monitoring of site safety compliance based on worker re-identification and personal protective equipment classification

4. Context-aware safety assessment system for far-field monitoring

5. SODA: A large-scale open site object detection dataset for deep learning in construction

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