{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:37:26Z","timestamp":1783183046422,"version":"3.54.6"},"reference-count":16,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,8]],"date-time":"2021-01-08T00:00:00Z","timestamp":1610064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>This article describes an AI-based solution to multiclass fire segmentation. The flame contours are divided into red, yellow, and orange areas. This separation is necessary to identify the hottest regions for flame suppression. Flame objects can have a wide variety of shapes (convex and non-convex). In that case, the segmentation task is more applicable than object detection because the center of the fire is much more accurate and reliable information than the center of the bounding box and, therefore, can be used by robotics systems for aiming. The UNet model is used as a baseline for the initial solution because it is the best open-source convolutional neural network. There is no available open dataset for multiclass fire segmentation. Hence, a custom dataset was developed and used in the current study, including 6250 samples from 36 videos. We compared the trained UNet models with several configurations of input data. The first comparison is shown between the calculation schemes of fitting the frame to one window and obtaining non-intersected areas of sliding window over the input image. Secondarily, we chose the best main metric of the loss function (soft Dice and Jaccard). We addressed the problem of detecting flame regions at the boundaries of non-intersected regions, and introduced new combinational methods of obtaining output signal based on weighted summarization and Gaussian mixtures of half-intersected areas as a solution. In the final section, we present UUNet-concatenative and wUUNet models that demonstrate significant improvements in accuracy and are considered to be state-of-the-art. All models use the original UNet-backbone at the encoder layers (i.e., VGG16) to demonstrate the superiority of the proposed architectures. The results can be applied to many robotic firefighting systems.<\/jats:p>","DOI":"10.3390\/sym13010098","type":"journal-article","created":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T23:03:42Z","timestamp":1610319822000},"page":"98","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["wUUNet: Advanced Fully Convolutional Neural Network for Multiclass Fire Segmentation"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2231-5711","authenticated-orcid":false,"given":"Vladimir Sergeevich","family":"Bochkov","sequence":"first","affiliation":[{"name":"Institute of Economics and Management, Nizhny Novgorod State Technical University n.a. R.E. Alexeev, 603950 Nizhny Novgorod, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9217-9324","authenticated-orcid":false,"given":"Liliya Yurievna","family":"Kataeva","sequence":"additional","affiliation":[{"name":"Institute of Economics and Management, Nizhny Novgorod State Technical University n.a. R.E. Alexeev, 603950 Nizhny Novgorod, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Saponara, S., Elhanashi, A., and Gagliardi, A. (2020). Real-time video fire\/smoke detection based on CNN in antifire surveillance systems. J. Real-Time Image Process., 1\u201313.","DOI":"10.1007\/s11554-020-01044-0"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2016). YOLO9000: Better, Faster, Stronger. arXiv.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dunnings, A., and Brecknon, T.P. (2018, January 7\u201310). Experimentally defined convolutional neural network architecture variants for non-temporal real-time fire detection. Proceedings of the International Conference on Image Processing, Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451657"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, H., and Guo, X. (2017, January 7\u201310). A novel fire detection approach based on CNN-SVM using tensorflow. Proceedings of the International Conference on Intelligent Computing, Liverpool, UK.","DOI":"10.1007\/978-3-319-63315-2_60"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Hartwig, A. (2018). Encoder-decoder with altrous separable convolution for semantic image segmentation. arXiv.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_6","first-page":"11533","article-title":"Fire segmentation using a DeepLabv3+ architecture","volume":"XXVI","author":"Harkat","year":"2020","journal-title":"Image Signal Process. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mlich, J., Kolpik, K., Hradis, M., and Zemcik, P. (2020, January 10\u201314). Fire segmentation in Still images. Proceedings of the International Conference on Advanced Concepts for Intelligent Vision Systems, Auckland, New Zealand.","DOI":"10.1007\/978-3-030-40605-9_3"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.proeng.2013.08.054","article-title":"Combustion chemistry and decomposition kinetics of forest fuels","volume":"62","author":"Korobeinichev","year":"2013","journal-title":"Procedia Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fisher, P., and Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. arXiv.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1134\/S001546281603011X","article-title":"On the laws of combustion wave suppression by free water in a homogeneous porous layer of organic combustive materials","volume":"51","author":"Kataeva","year":"2016","journal-title":"Fluid Dyn."},{"key":"ref_11","first-page":"1","article-title":"A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons","volume":"5","author":"Sorensen","year":"1948","journal-title":"Kongelie Dan. Vidensk. Selsk."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"297","DOI":"10.2307\/1932409","article-title":"Measures of the amount of ecologic association between species","volume":"26","author":"Dice","year":"1945","journal-title":"Ecology"},{"key":"ref_13","first-page":"547","article-title":"Etude comparative de la distribution florale dans une portion des Alpes et des Jura","volume":"37","author":"Jaccard","year":"1901","journal-title":"Bull. Soc. Vaudoise Sci. Nat."},{"key":"ref_14","unstructured":"Kingma, D., and Ba, J. (2014). Adam. A method for stochastic optimization. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Delving deep into rectifiers. Suprassing human-level performance on ImageNet classification. arXiv.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_16","unstructured":"Loshchilov, I., and Hutter, F. (2017, January 24\u201326). SGDR: Stochastic gradient descent with warm restarts. Proceedings of the International Conference on Learning Representations, Toulon, France."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/1\/98\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:08:22Z","timestamp":1760159302000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/1\/98"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,8]]},"references-count":16,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["sym13010098"],"URL":"https:\/\/doi.org\/10.3390\/sym13010098","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,8]]}}}