Automatic Vision-Based Dump Truck Productivity Measurement Based on Deep-Learning Illumination Enhancement for Low-Visibility Harsh Construction Environment-Reference-Cited by-同舟云学术

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Automatic Vision-Based Dump Truck Productivity Measurement Based on Deep-Learning Illumination Enhancement for Low-Visibility Harsh Construction Environment

Published:2024-11 Issue:11 Volume:150 Page:
ISSN:0733-9364
Container-title:Journal of Construction Engineering and Management
language:en
Short-container-title:J. Constr. Eng. Manage.

Author:

Deng Tao¹^ORCID,Sharafat Abubakar²^ORCID,Lee Soomin³,Seo Jongwon⁴

Affiliation:

1. Ph.D. Candidate, Dept. of Civil and Environmental Engineering, Hanyang Univ., Seoul 04763, Republic of Korea. ORCID:

2. Research Professor, School of Architectural, Civil, Environment, and Energy Engineering, Kyungpook National Univ., Daegu 41566, Republic of Korea. ORCID:

3. Ph.D. Candidate, Dept. of Civil and Environmental Engineering, Hanyang Univ., Seoul 04763, Republic of Korea.

4. Professor, Dept. of Civil and Environmental Engineering, Hanyang Univ., Seoul 04763, Republic of Korea (corresponding author).

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JCEMD4.COENG-14194

Reference56 articles.

1. Analysis of the retinex theory of color vision

2. Caterpillar. 2015. Caterpillar performance handbook. Peoria, IL: Catepillar Inc.

3. Automatic vision-based calculation of excavator earthmoving productivity using zero-shot learning activity recognition

4. Activity analysis of construction equipment using audio signals and support vector machines

5. Analytical review and evaluation of civil information modeling

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