Semantic-PolygonGraph driven context-aware coverage path planning for infrastructure visual inspection

Liu, Jiucai, Li, Haijiang ORCID: https://orcid.org/0000-0001-6326-8133, Wang, Dalei, Chai, Chengzhang ORCID: https://orcid.org/0000-0001-6911-8048 and Dong, Yiqing 2025. Semantic-PolygonGraph driven context-aware coverage path planning for infrastructure visual inspection. Advanced Engineering Informatics 68 (Part A) , 103580. 10.1016/j.aei.2025.103580 Item availability restricted.

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Abstract

Automatic infrastructure visual inspection using Unmanned Aerial Vehicles (UAVs) enhances efficiency and safety. However, existing approaches lack context-aware path planning capabilities, often leading to redundant inspections without focus. To address this limitation, this study introduces a novel infrastructure inspection paradigm that integrates 3D coverage path planning (3D-CPP), real-time data interpretation, and inspection information management, to generate and refine 3D-CPP progressively based on recorded information and real-time observation. The proposed paradigm consists of two main components. First, a graph-based information management system named Semantic-PolygonGraph is developed to incorporate static information from Industry Foundation Classes (IFC) alongside dynamically accumulated inspection data. Second, leveraging this structured representation, a progressive 3D-CPP method is proposed to generates an adaptive inspection path that dynamically refines itself based on task requirements, historical records, and real-time observations, prioritizing regions exhibiting superficial damage. To evaluate the effectiveness of the proposed paradigm, this study introduces a data quality assessment metric to quantify the trade-off between inspection cost and data quality. Simulated case studies demonstrate that the proposed approach improves data quality with limited increase of inspection costs, highlighting its potential for long-term infrastructure maintenance.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	Elsevier
ISSN:	1474-0346
Date of First Compliant Deposit:	4 July 2025
Date of Acceptance:	20 June 2025
Last Modified:	08 Jul 2025 13:15
URI:	https://orca.cardiff.ac.uk/id/eprint/179552

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