Automatic Detection of Water Supply Pipe Defects Based on Underwater Image Enhancement and Improved YOLOX-Reference-Cited by-同舟云学术

Automatic Detection of Water Supply Pipe Defects Based on Underwater Image Enhancement and Improved YOLOX

Published:2024-10 Issue:10 Volume:150 Page:
ISSN:0733-9364
Container-title:Journal of Construction Engineering and Management
language:en
Short-container-title:J. Constr. Eng. Manage.

Author:

Changwang Su¹,Shaowei Hu²,Haifen Zhang³,Fuqu Pan⁴,Changxi Shan¹,Hao Qi¹

Affiliation:

1. Ph.D. Student, Key Laboratory of New Technology for Construction of Cities in Mountain Area (Ministry of Education), School of Civil Engineering, Chongqing Univ., Chongqing 400045, China.

2. Professor of Pipeline Structure, Yellow River Laboratory, School of Water Resources and Civil Engineering, Zhengzhou Univ., Henan 450001, China (corresponding author).

3. Engineer, State Key Laboratory of Precision Testing Technology and Instrument, Tianjin Univ., Tianjin 300072, China.

4. Engineer, Shandong Dongxin Plastic Technology Co., Ltd., Bojiqiao St., Yanggu County, Shandong 252300, China.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JCEMD4.COENG-14919

Reference74 articles.

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2. Color Balance and Fusion for Underwater Image Enhancement

3. Bochkovskiy A. C. Y. Wang and H. Liao. 2020. “YOLOv4: Optimal speed and accuracy of object detection.” Preprint submitted April 23 2020. https://arxiv.org/abs/2004.10934.

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5. Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques