Abstract
Urban drainage pipeline plays a crucial role in the construction of modern cities, and Closed Circuit Television (CCTV) is one of the most commonly used technologies for pipeline inspection. However, CCTV is only in charge of reflecting the pipeline's status with videos and images. Identifying and evaluating pipeline defects still require professional knowledge and the participation of experienced practitioners. Therefore, intelligent approaches designed to improve the effectiveness and efficiency of pipeline inspection are deadly expected in practice. To address this issue, this paper presents a knowledge-driven approach with a prototype software tool. More specifically, one domain ontology is defined to formalize the required knowledge of pipeline defects identification, e.g., the types and classifications of pipeline defects. Furthermore, a set of reasoning rules for deducing pipeline defects and relevant defects parameters are designed to work with the proposed domain ontology. To verify the validity of our proposed domain ontology and reasoning rules, we conducted one industrial case study based on original images of pipeline defects provided by Nanjing BeiKong Enterprises Water Group Co., Ltd. Results show that defects in the selected pipeline images can be inferred correctly, which indicates that our proposed method can assist the automatic identification of pipeline defects.
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Acknowledgement
This work was partially supported by the Shandong Provincial Natural Science Foundation (No. ZR2021MF026) and the technical service project No. 1015-KFA21833. The authors would like to thank Nanjing BeiKong Enterprises Water Group Co., Ltd and the staff for their cooperation and assistance in providing industrial datasets.
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Mei, R., Wang, T., Qian, S., Zhang, H., Yan, X. (2022). Information Mining from Images of Pipeline Based on Knowledge Representation and Reasoning. In: Chen, W., Yao, L., Cai, T., Pan, S., Shen, T., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13726. Springer, Cham. https://doi.org/10.1007/978-3-031-22137-8_11
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