{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T16:45:20Z","timestamp":1770914720747,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T00:00:00Z","timestamp":1632700800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T00:00:00Z","timestamp":1632700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1007\/s00138-021-01242-1","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T15:03:11Z","timestamp":1632754991000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Squeezed fire binary segmentation model using convolutional neural network for outdoor images on embedded device"],"prefix":"10.1007","volume":"32","author":[{"given":"Kyungmin","family":"Song","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han-Soo","family":"Choi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Myungjoo","family":"Kang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,27]]},"reference":[{"key":"1242_CR1","doi-asserted-by":"crossref","unstructured":"Bogue, R.: Sensors for fire detection. Sensor Rev. 3(2), 99\u2013103 (2013)","DOI":"10.1108\/02602281311299635"},{"key":"1242_CR2","unstructured":"Thomas K.: Fire detection with temperature sensor arrays. In: Proceedings IEEE 34th Annual 2000 International Carnahan Conference on Security Technology (Cat. No. 00CH37083)"},{"issue":"8","key":"1242_CR3","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1016\/j.firesaf.2007.01.006","volume":"42","author":"Shin-Juh Chen","year":"2007","unstructured":"Chen, Shin-Juh., Hovde, David C., Peterson, Kristen A., Marshall, Andr\u00e9 W.: Fire detection using smoke and gas sensors. Fire Saf. J. 42(8), 507\u2013515 (2007)","journal-title":"Fire Saf. J."},{"issue":"5","key":"1242_CR4","doi-asserted-by":"publisher","first-page":"1249","DOI":"10.1007\/s10694-018-0727-x","volume":"54","author":"Xuanbing Qiu","year":"2018","unstructured":"Qiu, Xuanbing, Xi, Tingyu, Sun, Dongyuan, Zhang, Enhua, Li, Chuanliang, Peng, Ying, Wei, Jilin, Wang, Gao: Fire detection algorithm combined with image processing and flame emission spectroscopy. Fire Technol. 54(5), 1249\u20131263 (2018)","journal-title":"Fire Technol."},{"issue":"6","key":"1242_CR5","doi-asserted-by":"publisher","first-page":"881","DOI":"10.4218\/etrij.10.0109.0695","volume":"32","author":"Turgay Celik","year":"2010","unstructured":"Celik, Turgay: Fast and efficient method for fire detection using image processing. ETRI J. 32(6), 881\u2013890 (2010)","journal-title":"ETRI J."},{"issue":"5","key":"1242_CR6","doi-asserted-by":"publisher","first-page":"3113","DOI":"10.1109\/TII.2019.2897594","volume":"15","author":"M Khan","year":"2019","unstructured":"Khan, M., Salman, K., Mohamed, E., Syed, H.A., Sung, W.B.: Efficient fire detection for uncertain surveillance environment. IEEE Trans. Ind. Informatics 15(5), 3113\u20133122 (2019)","journal-title":"IEEE Trans. Ind. Informatics"},{"issue":"5","key":"1242_CR7","doi-asserted-by":"publisher","first-page":"1827","DOI":"10.1007\/s10694-019-00832-w","volume":"55","author":"Gaohua Lin","year":"2019","unstructured":"Lin, Gaohua, Zhang, Yongming, Gao, Xu., Zhang, Qixing: Smoke detection on video sequences using 3d convolutional neural networks. Fire Technol. 55(5), 1827\u20131847 (2019)","journal-title":"Fire Technol."},{"key":"1242_CR8","unstructured":"S\u00fcleyman, A., U\u011fur, G., B\u00a0U\u011fur, T., Enis \u00c7etin, A.: Deep convolutional generative adversarial networks based flame detection in video. arXiv:1902.01824 (2019)"},{"key":"1242_CR9","doi-asserted-by":"crossref","unstructured":"Sudhakar, S., Varadarajan Vijayakumar, C., Sathiya Kumar, V., Priya., Logesh, R., Subramaniyaswamy, V, : Unmanned aerial vehicle (uav) based forest fire detection and monitoring for reducing false alarms in forest-fires. Comput. Commun. 149, 1\u201316 (2020)","DOI":"10.1016\/j.comcom.2019.10.007"},{"key":"1242_CR10","doi-asserted-by":"crossref","unstructured":"Emmy Premal, C., Vinsley, S.S.: Image processing based forest fire detection using ycbcr colour model. Presented at the (2014)","DOI":"10.1109\/ICCPCT.2014.7054883"},{"key":"1242_CR11","unstructured":"Viktor, T., Romana, C.-H., Eva, T.: Forest fires detection in digital images based on color features. Int. J. Educ. Learn. Syst., 2, 66\u201370 (2017)"},{"key":"1242_CR12","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., Lawrence, C., Zitnick. (eds.): In: Microsoft coco: Common objects in context, pp. 740\u2013755. Springer (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"1242_CR13","unstructured":"Yuval N., Iacopo, M., Anh\u00a0Tran, T., Tal, H., Gerard, M.: On face segmentation, face swapping, and face perception. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 98\u2013105. IEEE (2018)"},{"issue":"4","key":"1242_CR14","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1080\/2150704X.2018.1557791","volume":"10","author":"Ye Li","year":"2019","unstructured":"Li, Ye., Lele, Xu., Rao, Jun, Guo, Lili, Yan, Zhen, Jin, Shan: A y-net deep learning method for road segmentation using high-resolution visible remote sensing images. Remote Sens. Lett. 10(4), 381\u2013390 (2019)","journal-title":"Remote Sens. Lett."},{"key":"1242_CR15","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.isprsjprs.2019.02.006","volume":"150","author":"W Michael","year":"2019","unstructured":"Michael, W., Thomas, S., Xiao, X.Z., Matthias, W., Hannes, T.: Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks. ISPRS J. Photogramm. Remote Sens. 150, 59\u201369 (2019)","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"1242_CR16","doi-asserted-by":"crossref","unstructured":"Yan, C., Shao, B., Zhao, H., Ning, R., Zhang, Y., Xu, F.: 3d room layout estimation from a single rgb image. IEEE Trans. Multimed. 22(11), 3014\u20133024 (2020)","DOI":"10.1109\/TMM.2020.2967645"},{"key":"1242_CR17","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: In: U-net: convolutional networks for biomedical image segmentation, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1242_CR18","unstructured":"Tran, M.Q., David, G.C.H, Won-Ki, J.: Fusionnet: a deep fully residual convolutional neural network for image segmentation in connectomics. arXiv:1612.05360 (2016)"},{"key":"1242_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2017.11.005","volume":"44","author":"Michal Drozdzal","year":"2018","unstructured":"Drozdzal, Michal, Chartrand, Gabriel, Vorontsov, Eugene, Shakeri, Mahsa, Di Jorio, Lisa, Tang, An., Romero, Adriana, Bengio, Yoshua, Pal, Chris, Kadoury, Samuel: Learning normalized inputs for iterative estimation in medical image segmentation. Med. Image Anal. 44, 1\u201313 (2018)","journal-title":"Med. Image Anal."},{"key":"1242_CR20","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. Presented at the (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"1242_CR21","doi-asserted-by":"crossref","unstructured":"Lane, N.D., Bhattacharya, S., Georgiev, P., Forlivesi, C., Jiao, L., Qendro, L., Kawsar, F., Deepx, (eds.): A software accelerator for low-power deep learning inference on mobile devices. Presented at the (2016)","DOI":"10.1109\/IPSN.2016.7460664"},{"key":"1242_CR22","unstructured":"Cazzolato, M.T., Avalhais, L.P.S., Chino, D.Y.T., Ramos, J.S., de Souza, J.A., Rodrigues-Jr, Jose, F., Traina, A.J.: Fismo: a compilation of datasets from emergency situations for fire and smoke analysis. Proc. Satell. events (2017)"},{"key":"1242_CR23","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.firesaf.2017.06.012","volume":"92","author":"T Tom","year":"2017","unstructured":"Tom, T., Lucile, R., Antoine, C., Turgay, C., Moulay Akhloufi, A.: An evolving image dataset for processing and analysis. Computer vision for wildfire research. Fire Saf. J. 92, 188\u2013194 (2017)","journal-title":"Fire Saf. J."},{"issue":"2","key":"1242_CR24","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.firesaf.2008.05.005","volume":"44","author":"Turgay Celik","year":"2009","unstructured":"Celik, Turgay, Demirel, Hasan: Fire detection in video sequences using a generic color model. Fire Saf. J. 44(2), 147\u2013158 (2009)","journal-title":"Fire Saf. J."},{"key":"1242_CR25","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. Presented at the (2015)","DOI":"10.1109\/ICCV.2015.178"},{"key":"1242_CR26","doi-asserted-by":"crossref","unstructured":"Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., Pal, C.: The importance of skip connections in biomedical image segmentation. In: Deep Learning and Data Labeling for Medical Applications, pp. 179\u2013187. Springer (2016)","DOI":"10.1007\/978-3-319-46976-8_19"},{"key":"1242_CR27","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.neucom.2019.05.011","volume":"357","author":"Feiniu Yuan","year":"2019","unstructured":"Yuan, Feiniu, Zhang, Lin, Xia, Xue, Wan, Boyang, Huang, Qinghua, Li, Xuelong: Deep smoke segmentation. Neurocomputing 357, 248\u2013260 (2019)","journal-title":"Neurocomputing"},{"key":"1242_CR28","unstructured":"Forrest, N.I., Song, H., Matthew, W.M., Khalid, A., William,\u00a0J.D., Kurt, K.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and 0.5 mb model size. arXiv:1602.07360 (2016)"},{"key":"1242_CR29","unstructured":"Andrew, G.H., Menglong, Z., Bo, C., Dmitry, K., Weijun, W., Tobias, W., Marco, A., Hartwig, A.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861 (2017)"},{"key":"1242_CR30","doi-asserted-by":"crossref","unstructured":"Novac, I., Geipel K.R., de Domingo Gil, J.E., de Paula, L.G., Hyttel, K., Chrysostomou, D.: A framework for wild-re inspection using deep convolutional neural networks. In: 2020 IEEE\/SICE International Symposium on System Integration (SII), pp 867\u2013872. IEEE (2020)","DOI":"10.1109\/SII46433.2020.9026244"},{"key":"1242_CR31","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C..: Mobilenetv 2: inverted residuals and linear bottlenecks. Presented at the (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"1242_CR32","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., Fergus, R.: In: Visualizing and understanding convolutional networks, pp. 818\u2013833. Springer (2014)","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"1242_CR33","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. Presented at the (2017)","DOI":"10.1109\/CVPR.2017.195"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-021-01242-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-021-01242-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-021-01242-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T15:05:25Z","timestamp":1635433525000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-021-01242-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,27]]},"references-count":33,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["1242"],"URL":"https:\/\/doi.org\/10.1007\/s00138-021-01242-1","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,27]]},"assertion":[{"value":"25 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"120"}}