Kızıl Ötesi Görüntülerden Binalardaki Isı Köprüsünün Belirlenmesi

Year 2024, Volume: 27 Issue: 3, 887 - 920, 25.07.2024

Önder Halis Bettemir

https://doi.org/10.2339/politeknik.1144858

Abstract

İnşaat sürecinde ısı yalıtımı imalatının kalitesi denetlenmediği için Türkiye’deki mevcut binaların büyük bir kısmının ısı yalıtım performansları bilinememektedir. Türkiye’deki yapı stoğunun büyüklüğü göz önüne alındığında binalardaki ısı yalıtım değerlerinin sıvanın kazınıp ısı yalıtım malzemesinin incelenmesi ile belirlenmesi uygulanabilir bir çözüm değildir. Bu çalışmada binalardaki ısı köprülerini binaların termal görüntülerini işleyerek belirleyen bir yöntem önerilmiştir. Yöntem termal görüntünün analiz edilerek yapı elemanlarının ısı kaybı var ve yok olarak sınıflandırılması ve termal görüntünün benirizasyonuna dayanmaktadır. Benirizasyon için adaptif yerel eşikleme ile küresel eşikleme yöntemleri uygulanmıştır. Uygulanan yöntemler sınıflandırma için bir eşik değerine ihtiyaç duymaktadır. Tüm görüntüler için geçerli bir eşik değeri belirlemek mümkün olmadığı için Otsu algoritması ile eşik değeri belirlenmiştir. Eşik belirleme işlemi termal görüntü üzerinde ve termal görüntüden elde edilen kenar görüntüleri üzerinde uygulanmıştır. Elde edilen eşik değerleri termal görüntü ve kenar görüntüleri üzerinde uygulanmıştır. Literatürden derlenen kenar belirleme algoritmaları beş adet termal görüntü incelenerek karşılaştırılmış ve Modifiye II Frei-Chen ve ikinci derece Laplace operatörü ile daha doğru sonuçlar elde edilmiştir. Önerilen yöntemin uygulanması ile mevut yapı stoğunun ısı yalıtım özelliğinin hızlı, ekonomik ve güvenilir biçimde tespit edilebileceği vaka çalışmaları sonucunda belirlenmiştir.

Keywords

Kenar belirleme, benirizasyon, otsu algoritması, Edge detection, binarization, otsu algorithm

Supporting Institution

İnönü Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü

Project Number

FBA-2018-1051

Determination of Thermal Bridge of the Buildings from Infrared Images

Abstract

Vast majority of the existing buildings in Türkiye are not inspected for thermal insulation quality during the construction process therefore, thermal insulation performance of the existing buildings cannot be known. Measuring the thermal insulation performance of the buildings by scraping the plaster and examining the heat insulation material is not a viable solution when the size of the building stock of Türkiye is considered. In this study, detection of thermal bridges of the buildings by processing the thermal images of the buildings is proposed. The method is based on the binarization of the thermal image by the classification of the building elements as heat loss element or no heat loss element by analyzing the thermal image of the building. Global threshold methods and adaptive local threshold methods applied for binarization. All of the implemented methods require a threshold value for the classification. Determining a valid threshold value for all images is not possible therefore the threshold value is determined by the Otsu algorithm. Threshold determination process is executed both on the thermal image and the edge image. Obtained threshold values are implemented on the thermal images and the edge images. Local edge detection algorithms derived from the literature are compared by examining five thermal images and the comparison revealed that the Modified II Frei-Chen and Second-order Laplace operator provided the most suitable result. The case studies revealed that the thermal insulation performance of the existing building stock can be determined quickly, economically and reliably by implementing the proposed method.

Keywords

Edge detection, binarization, otsu algorithm

Project Number

FBA-2018-1051

References

• [1] Öziç, M.Ü., Ekmekci, H., Özşen, S., Barstuğan, M., ve Yıldoğan, A., “3B T1 ağırlıklı MR görüntülerinde atlas tabanlı hacim ölçüm yöntemini kullanarak alzheimer hastalığının teşhisi”, Politeknik Dergisi, 25(1), 47-58, (2022).
• [2] İbrahım, M., “WBBA-KM: a hybrid weight-based bat algorithm with K-means algorithm for cluster analysis”, Politeknik Dergisi, 25(1), 65-73, (2022).
• [3] Wardlaw, J., Gryka, M., Wanner, F., Brostow, G., ve Kautz, J., “A new approach to thermal imaging visualisation”, EngD Group Project, University College London, (2010).
• [4] Lucchi, E., “Applications of the infrared thermography in the energy audit of buildings: A review”, Renewable and Sustainable Energy Reviews, 82, 3077-3090, (2018).
• [5] Kylili, A., Fokaides, P. A., Christou, P., ve Kalogirou, S. A., “Infrared thermography (IRT) applications for building diagnostics: A review”, Applied Energy, 134, 531-549, (2014).
• [6] Mayer, Z., Heuer, J., Volk, R., ve Schultmann, F., “Aerial thermographic image-based assessment of thermal bridges using representative classifications and calculations”, Energies, 14(21), 7360, (2021).
• [7] Mayer, Z., Kahn, J., Hou, Y., ve Volk, R., “AI-based thermal bridge detection of building rooftops on district scale using aerial images”, In Proceedings of the EG-ICE 2021 Workshop on Intelligent Computing in Engineering, Berlin, Germany (Vol. 30), (2021, June).
• [8] Hou, Y., Chen, M., Volk, R., ve Soibelman, L., “An approach to semantically segmenting building components and outdoor scenes based on multichannel aerial imagery datasets”, Remote Sensing, 13(21), 4357, (2021).
• [9] Hou, Y., Volk, R., ve Soibelman, L., “A novel building temperature simulation approach driven by expanding semantic segmentation training datasets with synthetic aerial thermal images”, Energies, 14(2), 353, (2021).
• [10] Arjoune, Y., Peri, S., Sugunaraj, N., Biswas, A., Sadhukhan, D., ve Ranganathan, P., “An Instance Segmentation and Clustering Model for Energy Audit Assessments in Built Environments: A Multi-Stage Approach”, Sensors, 21(13), 4375, (2021).
• [11] Pavlović, A., ve Barbarić, Ž., Application of G100/120 thermal imaging camera in energy efficiency measuring in building construction, Serbian Journal of Electrical Engineering, 10(1), 153-164, (2013).
• [12] O'Grady, M., Lechowska, A.A., ve Harte, A.M., “Application of infrared thermography technique to the thermal assessment of multiple thermal bridges and Windows”, Energy and Buildings, 168, 347- 362, (2018).
• [13] Kim, C., Choi, J. S., Jang, H., ve Kim, E. J., “Automatic detection of linear thermal bridges from infrared thermal images using neural network”, Applied Sciences, 11(3), 931, (2021).
• [14] Despotovic, M., Koch, D., Leiber, S., Doeller, M., Sakeena, M., ve Zeppelzauer, M., “Prediction and analysis of heating energy demand for detached houses by computer vision”, Energy and Buildings, 193, 29-35, (2019).
• [15] Martinez-De Dios, J. R., ve Ollero, A., “Automatic detection of windows thermal heat losses in buildings using UAVs”, In 2006 world automation congress (pp. 1-6). IEEE, (2006, July).
• [16] Guerriero, P., ve Daliento, S., “Automatic edge identification for accurate analysis of thermographic images of solar panels”, In 2017 6th International Conference on Clean Electrical Power (ICCEP), 768-772, IEEE, (2017, June).
• [17] Macher, H., Landes, T., ve Grussenmeyer, P. “Automation of Thermal Point Clouds Analysis for the Extraction Of Windows and Thermal Bridges of Building Facades”, The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 287-292, (2020).
• [18] Kakillioglu, B., Velipasalar, S., ve Rakha, T., “Autonomous heat leakage detection from unmanned aerial vehicle-mounted thermal cameras”, In Proceedings of the 12th International Conference on Distributed Smart Cameras, 1-6, (2018, September).
• [19] Rakha, T., Liberty, A., Gorodetsky, A., Kakillioglu, B., ve Velipasalar, S., “Heat mapping drones: an autonomous computer-vision-based procedure for building envelope inspection using unmanned aerial systems (UAS)”, Technology| Architecture+ Design, 2(1), 30-44, (2018).
• [20] Rakha, T., Liberty, A., Gorodetsky, A., Kakillioglu, B., ve Velipasalar, S., “Campus as a Lab for Computer Vision-based Heat Mapping Drones: A Case Study for Multiple Building Envelope Inspection using Unmanned Aerial Systems (UAS)”, (2018).
• [21] Taylor, T., Counsell, J., ve Gill, S., “Combining thermography and computer simulation to identify and assess insulation defects in the construction of building façades”, Energy and Buildings, 76, 130-142, (2014).
• [22] González-Aguilera, D., Lagueela, S., Rodríguez-Gonzálvez, P., ve Hernández-López, D., “Image-based thermographic modeling for assessing energy efficiency of buildings façades”, Energy and Buildings, 65, 29-36, (2013).
• [23] Lai, J. H. L., Lin, C. C., Chen, C. F. R., ve Lin, C. Y., “Multi-modality Mobile Image Recognition Based on Thermal and Visual Cameras”, In 2015 IEEE International Symposium on Multimedia (ISM), 477-482, (2015, December).
• [24] Lewandowski, W. M., Ryms, M., ve Denda, H., “Quantitative study of free convective heat losses from thermodynamic partitions using Thermal Imaging”, Energy and Buildings, 167, 370-383, (2018).
• [25] Rakha, T., ve Gorodetsky, A., “Review of Unmanned Aerial System (UAS) applications in the built environment: Towards automated building inspection procedures using drones”, Automation in Construction, 93, 252-264, (2018).
• [26] Nikzad, S., Kari, B. M., ve Tahmasebi, F., “The application of thermal imaging as a nondestructive test in historic buildings”, XII DBMC, Porto, Portugal, (2011)..
• [27] Ostańska, A., “Thermal imaging for detection of defects in envelopes of buildings in use: qualitative and quantitative analysis of building energy performance”, Periodica Polytechnica Civil Engineering, 62(4), 939-946, (2018).
• [28] Garrido, I., Lagüela, S., Arias, P., ve Balado, J., “Thermal-based analysis for the automatic detection and characterization of thermal bridges in buildings”, Energy and Buildings, 158, 1358-1367, (2018).
• [29] White, J. M., and Rohrer, G. D. “Image thresholding for optical character recognition and other applications requiring character image extraction”, IBM Journal of research and development, 27(4), 400-411, (1983).
• [30] Chow, C.K. ve Kaneko, T., “Automatic boundary detection of the left ventricle from cineangiograms”, Computers and biomedical research, 5(4), 388-410, (1972).
• [31] Taxt, T., Flynn, P.J. ve Jain, A.K., “Segmentation of document images”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(12), 1322-1329, (1989).
• [32] Mardia, K.V. ve Hainsworth, T.J., “A spatial thresholding method for image segmentation”, IEEE transactions on pattern analysis and machine intelligence, 10(6), 919-927, (1988).
• [33] Parker, J.R., “Gray level thresholding in badly illuminated images”, IEEE Transactions on Pattern Analysis & Machine Intelligence, 13(08), 813-819, (1991).
• [34] Yanowitz, S.D. ve Bruckstein, A.M., “A new method for image segmentation”, Computer Vision, Graphics, and Image Processing, 46(1), 82-95, (1989).
• [35] Trier, Ø.D., Jain, A.K., ve Taxt, T., “Feature extraction methods for character recognition-a survey”, Pattern recognition, 29(4), 641-662, (1996).
• [36] Cheriet, M., Said, J.N., ve Suen, C.Y., “A recursive thresholding technique for image segmentation”, IEEE transactions on image processing, 7(6), 918-921, (1998).
• [37] Bradley, D., ve Roth, G., “Adaptive thresholding using the integral image”, Journal of graphics tools, 12(2), 13-21, (2007).
• [38] Sezgin, M., ve Sankur, B., “Survey over image thresholding techniques and quantitative performance evaluation”, Journal of Electronic imaging, 13(1), 146-165, (2004).
• [39] Kittler, J., ve Illingworth, J., “Minimum error thresholding”, Pattern recognition, 19(1), 41-47, (1986).
• [40] Kittler, J., ve Illingworth, J., “On threshold selection using clustering criteria”, IEEE transactions on systems, man, and cybernetics, (5), 652-655, (1985).
• [41] Shafait, F., Keysers, D., ve Breuel, T. M., “Efficient implementation of local adaptive thresholding techniques using integral images”, In Document recognition and retrieval XV SPIE, 6815, 317-322, (2008, January).
• [42] Sauvola, J., ve Pietikäinen, M., “Adaptive document image binarization”, Pattern recognition, 33(2), 225-236, (2000).
• [43] Su, B., Lu, S., ve Tan, C. L., “Robust document image binarization technique for degraded document images”, IEEE transactions on image processing, 22(4), 1408-1417, (2012).
• [44] Trier, O.D., ve Taxt, T., “Evaluation of binarization methods for document images”, IEEE transactions on pattern analysis and machine intelligence, 17(3), 312-315, (1995).
• [45] Trier, O.D. ve Jain, A.K., “Goal-directed evaluation of binarization methods”, IEEE transactions on Pattern analysis and Machine Intelligence, 17(12), 1191-1201, (1995).
• [46] Niblack, W., “An introduction to digital image processing”, Strandberg Publishing Company, (1985).
• [47] Balaha, H.M., Ali, H.A., ve Badawy, M., “Automatic recognition of handwritten Arabic characters: a comprehensive review”, Neural Computing and Applications, 33(7), 3011-3034, (2021).
• [48] Kang, S., Iwana, B. K., ve Uchida, S. “Complex image processing with less data—Document image binarization by integrating multiple pre-trained U-Net modules”, Pattern Recognition, 109, 107577, (2021).
• [49] Bhowmik, S., Sarkar, R., Das, B., ve Doermann, D., “GiB: a Game theory Inspired Binarization technique for degraded document images”, IEEE Transactions on Image Processing, 28(3), 1443-1455, (2018).
• [50] Qureshi, R., Uzair, M., Khurshid, K., ve Yan, H., “Hyperspectral document image processing: Applications, challenges and future prospects”, Pattern Recognition, 90, 12-22, (2019).
• [51] Bettemir, Ö.H., “Bazı Yerel Benirizasyon Yöntemleri ile Binalarda Isı Kaybına Yol Açan Kısımların Belirlenmesi”, Anatolian Journal of Computer Sciences, 5(1), 22-30, (2020).
• [52] Cheremkhin, P. A., ve Kurbatova, E. A., “Comparative appraisal of global and local thresholding methods for binarisation of off-axis digital holograms” Optics and Lasers in Engineering, 115, 119- 130, (2019).
• [53] Cheremkhin, P.A. Kurbatova, E.A., Evtikhiev, N.N., Krasnov, V.V., Rodin, V.G., Starikov, R.S., “Adaptive Digital Hologram Binarization Method Based on Local Thresholding, Block Division and Error Diffusion”, Journal of Imaging, 8(2), 15, (2022).
• [54] Uçkan, T., Cengiz, H, ve Karci, A., “SSC: Clustering of Turkish texts by spectral graph partitioning”. Politeknik Dergisi, 24(4), 1433 -1444, (2021).
• [55] Bayram, F., “Derin öğrenme tabanlı otomatik plaka tanıma”, Politeknik Dergisi, 23(4), 955-960, (2020).
• [56] Cherri, A.K., ve Karim, M.A. “Optical symbolic substitution: edge detection using Prewitt, Sobel, and Roberts operators”, Applied Optics, 28(21), 4644-4648, (1989).
• [57] Kanopoulos, N., Vasanthavada, N. ve Baker, R.L., “Design of an image edge detection filter using the Sobel operator", IEEE Journal of solid-state circuits, 23(2), 358-367, (1988).
• [58] Abdou, I.E., ve Pratt, W.K., “Quantitative design and evaluation of enhancement /thresholding edge detectors”, Proceedings of the IEEE, 67(5), 753-763, (1979). [59] Robinson, G.S., “Edge detection by compass gradient masks”, Computer graphics and image processing, 6(5), 492-501, (1977).
• [60] Patel, B., Maheshwari, R.P., ve Raman, B., “Compass local binary patterns for gender recognition of facial photographs and sketches”, Neurocomputing, 218, 203-215, (2016).
• [61] Frei, W. ve Chen, C.C., “Fast boundary detection: A generalization and a new algorithm”, IEEE Transactions on computers, 100(10), 988-998, (1977).
• [62] Feng, Y., Zhang, J., ve Wang, S., “A new edge detection algorithm based on Canny idea”, In AIP Conference Proceedings AIP Publishing LLC, 1890(1), 040011, (2017, October).
• [63] Park, R. H., “One-dimensional frequency domain interpretation of compass roof edge and Frei–Chen line masks”, Pattern recognition letters, 20(3), 281-284, (1999).
• [64] Van Vliet, L. J., Young, I. T., ve Beckers, G. L., “An edge detection model based on non-linear laplace filtering”, In Machine Intelligence and Pattern Recognition, (7), 63-73, (1988).
• [65] Qu, Z., ve Zhang, L., “Research on image segmentation based on the improved Otsu algorithm”, In IEEE Second International Conference on Intelligent Human-Machine Systems and Cybernetics, (2), 228-231, (2010, August).
• [66] Trier, Ø. D., ve Taxt, T., “Improvement of “integrated function algorithm” for binarization of document images”, Pattern Recognition Letters, 16(3), 277-283, (1995).
• [67] Ding, L., ve Goshtasby, A., “On the Canny edge detector”, Pattern recognition, 34(3), 721-725, (2001).
• [68] Rong, W., Li, Z., Zhang, W., ve Sun, L., An improved CANNY edge detection algorithm. In 2014 IEEE international conference on mechatronics and automation, 577-582, (2014, August).
• [69] Otsu, N., “A threshold selection method from gray-level histograms”, IEEE transactions on systems, man, and cybernetics, 9(1), 62-66, (1979).
• [70] Bostancı, L., “Atık Cam Tozu İçeren Alkali–Aktive Edilmiş Cüruf Harçlarının Mekanik, Por Yapısı, Termal Yalıtkanlık ve Mikro Yapı Özellikleri”, Politeknik Dergisi, 25(1), 75 - 87.
• [71] Kaplan, G., Aruntaş, H. Y., “XPS yalıtımlı dış duvarların ısıl performanslarının deneysel incelenmesi”, Politeknik Dergisi, 24(2), 645-653, (2021).
• [72] Manav, A. Değişen mikro iklim koşullarında geleneksel konutların enerji etkin davranışları: geleneksel Mut evlerinin karşılaştırmalı değerlendirmesi. Politeknik Dergisi, 24(3), 1137-1149.
• [73] Sözen, A., Menlik, T, Anvari-Moghaddam, A. “Mapping of Turkey’s district heating/cooling requirements”, Politeknik Dergisi, 23(3), 867-878, (2020).
• [74] Kon O, İlhan, U., “Merkezi Isıtma Sistemlerinde Yerüstü ve Yeraltı Ön Yalıtımlı Boruların Optimum Yalıtım Kalınlığı, Enerji Tasarrufu ve Yakıt Emisyon Hesabı” Politeknik Dergisi, 25(1): 189-203, (2022).