Abstract
This paper deals with the problem of object detection from digital maps. We are interested in detecting objects in a map which are defined in the legend. We will explore different similarity measures to compare the legend objects to those detected in different areas of the map. Our object detection method is evaluated on maps representing wastewater networks. In particular, we are interested in the detection of objects that represent lifting stations and manholes. The ultimate goal, after detecting correctly the target objects, is to repair misfunctions or inconsistencies in the water supply or evacuation network. The experimental results show that our similarity measures give good accuracy results on the detection of the objects of the legends.
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References
Brunelli, R.: Template Matching Techniques in Computer Vision: Theory and Practice. Wiley Publishing (2009)
Chahinian, N., Delenne, C., Commandré, B., Derras, M., Deruelle, L., Bailly, J.S.: Automatic mapping of urban wastewater networks based on manhole cover locations. Comput. Environ. Urban Syst. 78 (2019)
Cox, G.F.: Template matching and measure of match in image processing (1995)
Jiuxiang, G., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., Chen, T.: Recent advances in convolutional neural networks. Pattern Recogn. 77, 354–377 (2018)
Hashemi, N.S., Aghdam, R.B., Ghiasi, A.S.B., Fatemi, P.: Template matching advances and applications in image analysis (2016)
Meyer, H., Pebesma, E.: Machine learning-based global maps of ecological variables and the challenge of assessing them. Nat. Commun. 13(1), 2208 (2022)
Hisham, M.B., Yaakob, S.N., Raof, R.A.A., Nazren, A.A., Wafi, N.M.: Template matching using sum of squared difference and normalized cross correlation 12 100–104 (2015)
Mounce, S.: A comparative study of artificial neural network architectures for time series prediction of water distribution system flow data 04 (2013)
Du Nguyen, H., Nguyen, T.Q.D., Thi, H.N., Lap, B.Q.: The use of machine learning algorithms for evaluating water quality index: a survey and perspective. In: 2022 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), pp. 1–6 (2022)
Nickels, K., Hutchinson, S.: Estimating uncertainty in SSD-based feature tracking (2002)
Scambos, T.A., Dutkiewicz, M.J., Wilson, J.C., Bindschadler, R.A.: Application of image cross-correlation to the measurement of glacier velocity using satellite image data. Remote Sens. Environ. 42(3), 177–186 (1992)
Sun, Y.: Root mean square minimum distance as a quality metric for stochastic optical localization nanoscopy images. Sci. Rep. 8(1), 17211 (2018)
Yongtao, Yu., Guan, H., Li, D., Jin, C., Wang, C., Li, J.: Road manhole cover delineation using mobile laser scanning point cloud data. IEEE Geosci. Remote Sens. Lett. 17(1), 152–156 (2019)
Zhu, K., Chen, Y., Ghamisi, P., Jia, X., Benediktsson, J.A.: Deep convolutional capsule network for hyperspectral image spectral and spectral-spatial classification. Remote Sens. 11(3), 223 (2019)
Acknowledgments
This research has received support from the European Union’s Horizon research and innovation programme under the MSCA (Marie Skłodowska-Curie Actions)-SE (Staff Exchanges) grant agreement 101086252; Call: HORIZON-MSCA-2021-SE-01, Project title: STARWARS (STormwAteR and WastewAteR networkS heterogeneous data AI-driven management). This research has also received support from the french national projet ANR CROQUIS (Collecte, représentation, complétion, fusion et interrogation de données de réseaux d’eau urbains hétérogènes et incertaines) project, grant ANR-21-CE23-0004 of the French research funding agency (Agence Nationale de la Recherche ANR).
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Marzinkowski, A., Benferhat, S., Paparrizou, A., Piette, C. (2024). On Object Detection Based on Similarity Measures from Digital Maps. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 824. Springer, Cham. https://doi.org/10.1007/978-3-031-47715-7_7
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DOI: https://doi.org/10.1007/978-3-031-47715-7_7
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