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Automatic Damage Segmentation Framework for Buried Sewer Pipes Based on Machine Vision: Case Study of Sewer Pipes in Zhengzhou, China
Research article (Journal of Infrastructure Systems, 2022) · cited 14× · AI/ML
Automatic Damage Segmentation Framework for Buried Sewer Pipes Based on Machine Vision: Case Study of Sewer Pipes in Zhengzhou, China
Summary
Automatic Damage Segmentation Framework for Buried Sewer Pipes Based on Machine Vision: Case Study of Sewer Pipes in Zhengzhou, China is a scholarly article[1].
Key Facts
Automatic Damage Segmentation Framework for Buried Sewer Pipes Based on Machine Vision: Case Study of Sewer Pipes in Zhengzhou, China's instance of is recorded as scholarly article[2].
References
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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Automatic Damage Segmentation Framework for Buried Sewer Pipes Based on Machine Vision: Case Study of Sewer Pipes in Zhengzhou, China. Retrieved May 24, 2026, from https://4ort.xyz/entity/automatic-damage-segmentation-framework-for-buried-sewer-pipes-based-on-machine-vision-case-study-of-sewer-pipes-in-zhen
MLA“Automatic Damage Segmentation Framework for Buried Sewer Pipes Based on Machine Vision: Case Study of Sewer Pipes in Zhengzhou, China.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/automatic-damage-segmentation-framework-for-buried-sewer-pipes-based-on-machine-vision-case-study-of-sewer-pipes-in-zhen.
BibTeX@misc{4ortxyz_automatic-damage-segmentation-framework-for-buried-sewer-pipes-based-on-machine-vision-case-study-of-sewer-pipes-in-zhen_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Automatic Damage Segmentation Framework for Buried Sewer Pipes Based on Machine Vision: Case Study of Sewer Pipes in Zhengzhou, China}}, year = {2026}, url = {https://4ort.xyz/entity/automatic-damage-segmentation-framework-for-buried-sewer-pipes-based-on-machine-vision-case-study-of-sewer-pipes-in-zhen}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Automatic Damage Segmentation Framework for Buried Sewer Pipes Based on Machine Vision: Case Study of Sewer Pipes in Zhengzhou, China — https://4ort.xyz/entity/automatic-damage-segmentation-framework-for-buried-sewer-pipes-based-on-machine-vision-case-study-of-sewer-pipes-in-zhen (retrieved 2026-05-24)
