{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T15:46:27Z","timestamp":1774021587049,"version":"3.50.1"},"reference-count":24,"publisher":"Tech Science Press","issue":"2","license":[{"start":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T00:00:00Z","timestamp":1732406400000},"content-version":"vor","delay-in-days":328,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2024]]},"DOI":"10.32604\/cmc.2024.057118","type":"journal-article","created":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T06:43:32Z","timestamp":1730789012000},"page":"2261-2279","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing Building Facade Image Segmentation via Object-Wise Processing and Cascade U-Net"],"prefix":"10.32604","volume":"81","author":[{"given":"Haemin","family":"Jung","sequence":"first","affiliation":[]},{"given":"Heesung","family":"Park","sequence":"additional","affiliation":[]},{"given":"Hae Sun","family":"Jung","sequence":"additional","affiliation":[]},{"given":"Kwangyon","family":"Lee","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2024]]},"reference":[{"key":"ref1","unstructured":"U.S. Department of Energy, \u201cChapter 5\u2014Increasing efficiency of building systems and technologies,\u201d Sep. 2015. Accessed: Aug. 8, 2024. [Online]. Available: https:\/\/www.energy.gov\/articles\/chapter-5-increasing-efficiency-buildings-systems-and-technologies"},{"key":"ref2","article-title":"Global status report for buildings and construction\u2014Beyond foundations: Mainstreaming sustainable solutions to cut emissions from the buildings sector","year":"2024","journal-title":"Technical Reports"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"H. Liu, J. Zhang, J. Zhu, and S. C. Hoi, \u201cDeepFacade: A deep learning approach to facade parsing,\u201d presented at the 26th Int. Joint Conf. Artif. Intell. (IJCAI-17), Melbourne, Australia, Aug. 19\u201325, 2017, pp. 2301\u20132307.","DOI":"10.24963\/ijcai.2017\/320"},{"key":"ref4","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2021.103627","article-title":"Thermal anomaly detection in walls via CNN-based segmentation","volume":"125","author":"Park","year":"2021, Art. no. 103627","journal-title":"Autom. Constr."},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"L. C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, \u201cEncoder-decoder with atrous separable convolution for semantic image segmentation,\u201d presented at the Eur. Conf. Comput. Vis. (ECCV), Munich, Germany, Sep. 8\u201314, 2018, pp. 801\u2013818.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"O. Ronneberger, P. Fischer, and T. Brox, \u201cU-Net: Convolutional networks for biomedical image segmentation,\u201d presented at the Med. Image Comput. Comput.-Assist. Interv. (MICCAI 2015), Munich, Germany, Oct. 5\u20139, 2015, vol. 9351, pp. 234\u2013241. doi: 10.1007\/978-3-319-24574-4_28.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref7","article-title":"Automated crack segmentation in close-range building fa\u00e7ade inspection images using deep learning techniques","volume":"43","author":"Chen","year":"2021, Art. no. 102913","journal-title":"J. Build. Eng."},{"key":"ref8","unstructured":"L. C. Chen, G. Papandreou, F. Schroff, and H. Adam, \u201cRethinking atrous convolution for semantic image segmentation,\u201d 2017, arXiv:1706.05587."},{"key":"ref9","unstructured":"K. Sun et al., \u201cHigh-resolution representations for labeling pixels and regions,\u201d 2019, arXiv:1904.04514."},{"key":"ref10","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1109\/JSTSP.2020.3001502","article-title":"BB-UNet: U-Net with bounding box prior","volume":"14","author":"El Jurdi","year":"2020","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2021.3064606","article-title":"HED-UNet: Combined segmentation and edge detection for monitoring the Antarctic coastline","volume":"60","author":"Heidler","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref12","unstructured":"A. Vaswani et al., \u201cAttention is all you need,\u201d presented at the 31st Conf. Neural Inform. Process. Syst. (NeurIPS 2017), Long Beach, CA, USA, Dec. 4\u20139, 2017, vol. 30."},{"key":"ref13","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A computational approach to edge detection","volume":"PAMI-8","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"S. Xie and Z. Tu, \u201cHolistically-nested edge detection,\u201d presented at the IEEE Int. Conf. Comput. Vis. (ICCV), Santiago, Chile, Dec. 11\u201318, 2015, pp. 1395\u20131403. doi: 10.1109\/ICCV.2015.164.","DOI":"10.1109\/ICCV.2015.164"},{"key":"ref15","unstructured":"X. S. Poma, A. Sappa, P. Humanante, and A. Arbarinia, \u201cDense extreme inception network for edge detection,\u201d 2021, arXiv:2112.02250."},{"key":"ref16","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TPAMI.2010.161","article-title":"Contour detection and hierarchical image segmentation","volume":"33","author":"Arbelaez","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref17","unstructured":"H. Kervadec, J. Dolz, S. Wang, E. Granger, and I. B. Ayed, \u201cBounding boxes for weakly supervised segmentation: Global constraints get close to full supervision,\u201d presented at the Med. Imaging Deep Learn. (MIDL), Montreal, QC, Canada, Jul. 6\u20138, 2020, pp. 365\u2013381."},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"J. Lee, J. Yi, C. Shin, and S. Yoon, \u201cBBAM: Bounding box attribution map for weakly supervised semantic and instance segmentation,\u201d presented at the IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Nashville, TN, USA, Jun. 19\u201325, 2021, pp. 2643\u20132652.","DOI":"10.1109\/CVPR46437.2021.00267"},{"key":"ref19","first-page":"120","article-title":"The OpenCV library","volume":"25","author":"Bradski","year":"2000","journal-title":"Dr. Dobb's J. Softw. Tools"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"R. Tyle\u010dek and R. \u0160\u00e1ra, \u201cSpatial pattern templates for recognition of objects with regular structure,\u201d presented at the 35th German Conf. Pattern Recognit. (GCPR), Saarbr\u00fccken, Germany, Sep. 3\u20136, 2013, pp. 364\u2013374.","DOI":"10.1007\/978-3-642-40602-7_39"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"R. Liu, Z. Li, and J. Jia, \u201cImage partial blur detection and classification,\u201d presented at the IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Anchorage, AK, USA, Jun. 23\u201328, 2008, pp. 1\u20138. doi: 10.1109\/CVPR.2008.4587465.","DOI":"10.1109\/CVPR.2008.4587465"},{"key":"ref22","unstructured":"H. Inoue, \u201cData augmentation by pairing samples for images classification,\u201d 2018, arXiv:1801.02929."},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe and Y. Yoo, \u201cCutMix: Regularization strategy to train strong classifiers with localizable features,\u201d presented at the IEEE\/CVF Int. Conf. Comput. Vis. (ICCV), Seoul, Republic of Korea, Oct. 27\u2013Nov. 2, 2019, pp. 6023\u20136032.","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d presented at the IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas, NV, USA, Jun. 26\u201330, 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.techscience.com\/files\/cmc\/2024\/TSP_CMC-81-2\/TSP_CMC_57118\/TSP_CMC_57118.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T04:21:01Z","timestamp":1741321261000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v81n2\/58670"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":24,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024]]},"published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2024.057118","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"2024-08-08","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-07","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-18","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}