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    Research on Improved YOLOv5 Pipeline Defect Detection Algorithm 

    Source: Journal of Pipeline Systems Engineering and Practice:;2025:;Volume ( 016 ):;issue: 002:;page 04024073-1
    Author(s): JiangChao Zeng; YiMing Zheng; XinPing Jin; JinHong Lin; YongHao Feng
    Publisher: American Society of Civil Engineers
    Abstract: The main problem addressed in this research is the detection of three types of defects in underground drainage pipelines: gravel intrusion, obstacles, and foreign objects. To tackle this, improvements have been made by ...
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    DSpace software copyright © 2002-2015  DuraSpace
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