{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T13:16:57Z","timestamp":1783430217476,"version":"3.54.6"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T00:00:00Z","timestamp":1782345600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T00:00:00Z","timestamp":1782345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2025M770290"],"award-info":[{"award-number":["2025M770290"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of Henan Province","doi-asserted-by":"publisher","award":["252300423316"],"award-info":[{"award-number":["252300423316"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1007\/s11760-026-05512-1","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T02:11:57Z","timestamp":1782353517000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MSTL-YOLO: sparse transformer-enhanced lightweight instance segmentation for UAV infrared fire reconnaissance"],"prefix":"10.1007","volume":"20","author":[{"given":"Sen","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinbo","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojie","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lifan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,25]]},"reference":[{"key":"5512_CR1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3515215","author":"C Mambile","year":"2024","unstructured":"Mambile, C., Kaijage, S., Leo, J.: Application of deep learning in forest fire prediction: a systematic review. IEEE Access (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3515215","journal-title":"IEEE Access"},{"issue":"10","key":"5512_CR2","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1038\/s43017-020-0085-3","volume":"1","author":"DM Bowman","year":"2020","unstructured":"Bowman, D.M., Kolden, C.A., Abatzoglou, J.T., Johnston, F.H., Werf, G.R., Flannigan, M.: Vegetation fires in the anthropocene. Nat. Rev. Earth Environ.. 1(10), 500\u2013515 (2020). https:\/\/doi.org\/10.1038\/s43017-020-0085-3","journal-title":"Nat. Rev. Earth Environ.."},{"key":"5512_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2023.3329509","volume":"21","author":"Z Qadir","year":"2023","unstructured":"Qadir, Z., Le, K., Bao, V.N.Q., Tam, V.W.: Deep learning-based intelligent post-bushfire detection using UAVs. IEEE Geosci. Remote Sens. Lett. 21, 1\u20135 (2023). https:\/\/doi.org\/10.1109\/LGRS.2023.3329509","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"2","key":"5512_CR4","doi-asserted-by":"publisher","first-page":"2241","DOI":"10.1109\/JIOT.2018.2887086","volume":"6","author":"B Li","year":"2018","unstructured":"Li, B., Fei, Z., Zhang, Y.: UAV communications for 5G and beyond: Recent advances and future trends. IEEE Internet Things J. 6(2), 2241\u20132263 (2018). https:\/\/doi.org\/10.1109\/JIOT.2018.2887086","journal-title":"IEEE Internet Things J."},{"issue":"2","key":"5512_CR5","doi-asserted-by":"publisher","first-page":"1004","DOI":"10.1109\/TGRS.2019.2942384","volume":"58","author":"H Zhu","year":"2019","unstructured":"Zhu, H., Liu, S., Deng, L., Li, Y., Xiao, F.: Infrared small target detection via low-rank tensor completion with top-hat regularization. IEEE Trans. Geosci. Remote Sens. 58(2), 1004\u20131016 (2019). https:\/\/doi.org\/10.1109\/TGRS.2019.2942384","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"5512_CR6","doi-asserted-by":"publisher","unstructured":"Li, Q., Xiang, Q., Xie, Y.: Improved YOLOv4-tiny for fire detection. In: 2024 20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 1\u20135 (2024). https:\/\/doi.org\/10.1109\/ICNC-FSKD64080.2024.10702272 . IEEE","DOI":"10.1109\/ICNC-FSKD64080.2024.10702272"},{"issue":"4","key":"5512_CR7","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1139\/er-2020-0019","volume":"28","author":"P Jain","year":"2020","unstructured":"Jain, P., Coogan, S.C., Subramanian, S.G., Crowley, M., Taylor, S., Flannigan, M.D.: A review of machine learning applications in wildfire science and management. Environ. Rev. 28(4), 478\u2013505 (2020). https:\/\/doi.org\/10.1139\/er-2020-0019","journal-title":"Environ. Rev."},{"issue":"18","key":"5512_CR8","doi-asserted-by":"publisher","first-page":"13849","DOI":"10.1109\/JIOT.2021.3088875","volume":"8","author":"Z Chang","year":"2021","unstructured":"Chang, Z., Liu, S., Xiong, X., Cai, Z., Tu, G.: A survey of recent advances in edge-computing-powered artificial intelligence of things. IEEE Internet Things J. 8(18), 13849\u201313875 (2021). https:\/\/doi.org\/10.1109\/JIOT.2021.3088875","journal-title":"IEEE Internet Things J."},{"issue":"8","key":"5512_CR9","doi-asserted-by":"publisher","first-page":"1655","DOI":"10.1109\/JPROC.2019.2921977","volume":"107","author":"J Chen","year":"2019","unstructured":"Chen, J., Ran, X.: Deep learning with edge computing: A review. Proc. IEEE 107(8), 1655\u20131674 (2019). https:\/\/doi.org\/10.1109\/JPROC.2019.2921977","journal-title":"Proc. IEEE"},{"issue":"6","key":"5512_CR10","doi-asserted-by":"publisher","DOI":"10.1117\/1.2748752","volume":"46","author":"BU Toreyin","year":"2007","unstructured":"Toreyin, B.U., Cinbis, R.G., Dedeoglu, Y., Cetin, A.E.: Fire detection in infrared video using wavelet analysis. Opt. Eng. 46(6), 067204 (2007). https:\/\/doi.org\/10.1117\/1.2748752","journal-title":"Opt. Eng."},{"key":"5512_CR11","doi-asserted-by":"publisher","unstructured":"Cetin, A.E., Merci, B., Gunay, O., Toreyin, B.U., Verstockt, S.: Methods and Techniques for Fire Detection: Signal, Image and Video Processing Perspectives. Academic Press, Amsterdam, Netherlands (2016). https:\/\/doi.org\/10.1016\/C2014-0-01269-5","DOI":"10.1016\/C2014-0-01269-5"},{"issue":"8","key":"5512_CR12","doi-asserted-by":"publisher","first-page":"6251","DOI":"10.1007\/s11760-024-03311-0","volume":"18","author":"H Gupta","year":"2024","unstructured":"Gupta, H., Nihalani, N.: An efficient fire detection system based on deep neural network for real-time applications. SIViP 18(8), 6251\u20136264 (2024). https:\/\/doi.org\/10.1007\/s11760-024-03311-0","journal-title":"SIViP"},{"issue":"11","key":"5512_CR13","doi-asserted-by":"publisher","first-page":"8327","DOI":"10.1007\/s11760-024-03476-8","volume":"18","author":"F Sun","year":"2024","unstructured":"Sun, F., He, N., Wang, X., Liu, H., Zou, Y.: YOLOv7-P: a lighter and more effective UAV aerial photography object detection algorithm. SIViP 18(11), 8327\u20138335 (2024). https:\/\/doi.org\/10.1007\/s11760-024-03476-8","journal-title":"SIViP"},{"key":"5512_CR14","doi-asserted-by":"publisher","unstructured":"Srinivas, K., Dua, M.: Fog computing and deep CNN based efficient approach to early forest fire detection with unmanned aerial vehicles. In: International Conference on Inventive Computation Technologies, pp. 646\u2013652 (2019). https:\/\/doi.org\/10.1007\/978-3-030-33846-6_69 . Springer","DOI":"10.1007\/978-3-030-33846-6_69"},{"key":"5512_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2025.3528549","author":"S Chaturvedi","year":"2025","unstructured":"Chaturvedi, S., Thakur, P.S., Khanna, P., Ojha, A., Song, Y., Awange, J.L.: Satellite image-based surveillance and early wildfire smoke detection using a multiattention interlaced network. IEEE Trans. Industr. Inf. (2025). https:\/\/doi.org\/10.1109\/TII.2025.3528549","journal-title":"IEEE Trans. Industr. Inf."},{"key":"5512_CR16","doi-asserted-by":"publisher","unstructured":"Barmpoutis, P., Dimitropoulos, K., Kaza, K., Grammalidis, N.: Fire detection from images using faster R-CNN and multidimensional texture analysis. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8301\u20138305 (2019). https:\/\/doi.org\/10.1109\/ICASSP.2019.8682647 . IEEE","DOI":"10.1109\/ICASSP.2019.8682647"},{"key":"5512_CR17","doi-asserted-by":"publisher","unstructured":"Wang, M., Yue, P., Jiang, L., Yu, D., Tuo, T., Li, J.: An open flame and smoke detection dataset for deep learning in remote sensing based fire detection. Geo-spatial Information Science 28(2), 511\u2013526 (2025). https:\/\/doi.org\/10.1080\/10095020.2024.2347922","DOI":"10.1080\/10095020.2024.2347922"},{"issue":"2","key":"5512_CR18","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1109\/TPAMI.2018.2844175","volume":"42","author":"K He","year":"2020","unstructured":"He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 386\u2013397 (2020). https:\/\/doi.org\/10.1109\/TPAMI.2018.2844175","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5512_CR19","doi-asserted-by":"publisher","unstructured":"Bolya, D., Zhou, C., Xiao, F., Lee, Y.J.: YOLACT: Real-time instance segmentation. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9156\u20139165 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00925 . IEEE","DOI":"10.1109\/ICCV.2019.00925"},{"issue":"7","key":"5512_CR20","doi-asserted-by":"publisher","first-page":"3523","DOI":"10.1109\/TPAMI.2021.3059968","volume":"44","author":"S Minaee","year":"2021","unstructured":"Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., Terzopoulos, D.: Image segmentation using deep learning: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(7), 3523\u20133542 (2021). https:\/\/doi.org\/10.1109\/TPAMI.2021.3059968","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"5512_CR21","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","volume":"45","author":"K Han","year":"2022","unstructured":"Han, K., Wang, Y., Chen, H., Chen, X., Guo, J., Liu, Z., Tang, Y., Xiao, A., Xu, C.: A survey on vision transformer. IEEE Trans. Pattern Anal. Mach. Intell. 45(1), 87\u2013110 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2022.3152247","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5512_CR22","doi-asserted-by":"publisher","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9992\u201310002 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"5512_CR23","doi-asserted-by":"publisher","unstructured":"Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., Shao, L.: Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 568\u2013578 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00061","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"5512_CR24","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Advances in Neural Information Processing Systems, vol. 34, pp. 12077\u201312090. (2021)"},{"key":"5512_CR25","doi-asserted-by":"publisher","unstructured":"Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1290\u20131299 (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.00135","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"5512_CR26","unstructured":"Jocher, G., Qiu, J.: Ultralytics YOLO26. https:\/\/github.com\/ultralytics\/ultralytics. Version 26.0.0 (2026)"},{"issue":"2","key":"5512_CR27","doi-asserted-by":"publisher","first-page":"1655","DOI":"10.1109\/TCSVT.2025.3601598","volume":"36","author":"Y Xue","year":"2026","unstructured":"Xue, Y., Jin, G., Zhong, B., Shen, T., Tan, L., Xue, C., Zheng, Y.: Fmtrack: Frequency-aware interaction and multi-expert fusion for rgb-t tracking. IEEE Trans. Circuits Syst. Video Technol. 36(2), 1655\u20131667 (2026). https:\/\/doi.org\/10.1109\/TCSVT.2025.3601598","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"5512_CR28","doi-asserted-by":"publisher","unstructured":"Zhang, X., Liu, G., Huang, L., Ren, Q., Bavirisetti, D.P.: Ivomfuse: An image fusion method based on infrared-to-visible object mapping. Digital Signal Processing 137, 104032 (2023) https:\/\/doi.org\/10.1016\/j.dsp.2023.104032","DOI":"10.1016\/j.dsp.2023.104032"},{"key":"5512_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2024.104801","volume":"156","author":"X Zhang","year":"2025","unstructured":"Zhang, X., Liu, G., Li, M., Ren, Q., Tang, H., Bavirisetti, D.P.: Fusionngfpe: An image fusion approach driven by non-global fuzzy pre-enhancement framework. Digital Signal Processing 156, 104801 (2025). https:\/\/doi.org\/10.1016\/j.dsp.2024.104801","journal-title":"Digital Signal Processing"},{"issue":"2","key":"5512_CR30","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1109\/TG.2023.3263001","volume":"16","author":"X Gu","year":"2024","unstructured":"Gu, X., Liu, G., Zhang, X., Tang, L., Zhou, X., Qiu, W.: Infrared-visible synthetic data from game engine for image fusion improvement. IEEE Transactions on Games 16(2), 291\u2013302 (2024). https:\/\/doi.org\/10.1109\/TG.2023.3263001","journal-title":"IEEE Transactions on Games"},{"key":"5512_CR31","doi-asserted-by":"publisher","unstructured":"Hu, J., Bai, T., Wu, F., Peng, Z., Zhang, Y.: P$$^2$$hct: Plug-and-play hierarchical c2f transformer for multi-scale feature fusion. arXiv preprint arXiv:2505.12772 (2025) https:\/\/doi.org\/10.48550\/arXiv.2505.12772","DOI":"10.48550\/arXiv.2505.12772"},{"key":"5512_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108001","volume":"193","author":"A Shamsoshoara","year":"2021","unstructured":"Shamsoshoara, A., Afghah, F., Razi, A., Zheng, L., Ful\u00e9, P.Z., Blasch, E.: Aerial imagery pile burn detection using deep learning: The FLAME dataset. Comput. Netw. 193, 108001 (2021). https:\/\/doi.org\/10.1016\/j.comnet.2021.108001","journal-title":"Comput. Netw."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05512-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-026-05512-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05512-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T12:45:17Z","timestamp":1783428317000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-026-05512-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,25]]},"references-count":32,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["5512"],"URL":"https:\/\/doi.org\/10.1007\/s11760-026-05512-1","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,25]]},"assertion":[{"value":"12 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 June 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}}],"article-number":"449"}}