{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T10:14:00Z","timestamp":1776075240351,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"8-9","license":[{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The Practice Innovation Training Program Projects for Jiangsu College Students","award":["MOE2013-T2-1-041"],"award-info":[{"award-number":["MOE2013-T2-1-041"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s11760-024-03288-w","type":"journal-article","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T13:03:18Z","timestamp":1717419798000},"page":"6007-6017","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["CF-YOLO: a capable forest fire identification algorithm founded on YOLOv7 improvement"],"prefix":"10.1007","volume":"18","author":[{"given":"Wanjie","family":"Liu","sequence":"first","affiliation":[]},{"given":"Zirui","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Sheng","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,3]]},"reference":[{"key":"3288_CR1","doi-asserted-by":"crossref","unstructured":"Wang, T., Su, J., Huang, Y. et\u00a0al.: Study of the pseudo-color processing for infrared forest-fire image. In: 2010 2nd international conference on future computer and communication, pp V1\u2013415. IEEE (2010)","DOI":"10.1109\/ICFCC.2010.5497756"},{"key":"3288_CR2","doi-asserted-by":"crossref","unstructured":"Zou, Z., Chen, K., Shi, Z., et\u00a0al.: Object detection in 20 years: a survey. In: Proceedings of the IEEE (2023)","DOI":"10.1109\/JPROC.2023.3238524"},{"key":"3288_CR3","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., et\u00a0al.: Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"},{"key":"3288_CR4","unstructured":"Sermanet, P., Eigen, D., Zhang, X., et\u00a0al.: Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv:1312.6229 (2013)"},{"key":"3288_CR5","doi-asserted-by":"crossref","unstructured":"Kim, E., Kim, S., Seo, M., et\u00a0al.: Xprotonet: diagnosis in chest radiography with global and local explanations. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 15719\u201315728 (2021)","DOI":"10.1109\/CVPR46437.2021.01546"},{"key":"3288_CR6","doi-asserted-by":"crossref","unstructured":"Wu, Y., Lim, J., Yang, M.H.: Online object tracking: A benchmark. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2411\u20132418 (2013)","DOI":"10.1109\/CVPR.2013.312"},{"key":"3288_CR7","doi-asserted-by":"crossref","unstructured":"Xu, D., Huang, Q., Liu, H.: Object detection on robot operation system. In: 2016 IEEE 11th conference on industrial electronics and applications (ICIEA), pp 1155\u20131159. IEEE (2016)","DOI":"10.1109\/ICIEA.2016.7603758"},{"issue":"14","key":"3288_CR8","doi-asserted-by":"publisher","first-page":"3224","DOI":"10.3390\/s19143224","volume":"19","author":"PR Palafox","year":"2019","unstructured":"Palafox, P.R., Betz, J., Nobis, F., et al.: Semanticdepth: Fusing semantic segmentation and monocular depth estimation for enabling autonomous driving in roads without lane lines. Sensors 19(14), 3224 (2019)","journal-title":"Sensors"},{"issue":"2","key":"3288_CR9","doi-asserted-by":"publisher","first-page":"024523","DOI":"10.1117\/1.JRS.15.024523","volume":"15","author":"Q Li","year":"2021","unstructured":"Li, Q., Yuan, P., Lin, Y., et al.: Pointwise classification of mobile laser scanning point clouds of urban scenes using raw data. J. Appl. Remote Sens. 15(2), 024523\u2013024523 (2021)","journal-title":"J. Appl. Remote Sens."},{"key":"3288_CR10","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.isprsjprs.2019.11.023","volume":"159","author":"K Li","year":"2020","unstructured":"Li, K., Wan, G., Cheng, G., et al.: Object detection in optical remote sensing images: a survey and a new benchmark. ISPRS J. Photogramm. Remote. Sens. 159, 296\u2013307 (2020)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"issue":"2","key":"3288_CR11","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1080\/01431161.2024.2302351","volume":"45","author":"Q Wang","year":"2024","unstructured":"Wang, Q., Hu, C., Wang, H., et al.: Semantic segmentation of urban land classes using a multi-scale dataset. Int. J. Remote Sens. 45(2), 653\u2013675 (2024)","journal-title":"Int. J. Remote Sens."},{"key":"3288_CR12","doi-asserted-by":"crossref","unstructured":"Tang, X., Du, D.K., He, Z., et\u00a0al.: Pyramidbox: A context-assisted single shot face detector. In: Proceedings of the European conference on computer vision (ECCV), pp. 797\u2013813 (2018)","DOI":"10.1007\/978-3-030-01240-3_49"},{"key":"3288_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3355422","author":"Q Ye","year":"2024","unstructured":"Ye, Q., Yang, J., Zheng, H., et al.: Comments and correctionsconvergence analysis on trace ratio linear discriminant analysis algorithms. IEEE Trans. Neural Netw. Learn. Syst. (2024). https:\/\/doi.org\/10.1109\/TNNLS.2024.3355422","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"7553","key":"3288_CR14","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. nature 521(7553), 436\u2013444 (2015)","journal-title":"nature"},{"key":"3288_CR15","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","volume":"57","author":"P Viola","year":"2004","unstructured":"Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vision 57, 137\u2013154 (2004)","journal-title":"Int. J. Comput. Vision"},{"key":"3288_CR16","doi-asserted-by":"crossref","unstructured":"Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201905), pp 886\u2013893. IEEE (2005)","DOI":"10.1109\/CVPR.2005.177"},{"key":"3288_CR17","unstructured":"Ruder, S.: An overview of gradient descent optimization algorithms. arXiv:1609.04747 (2016)"},{"key":"3288_CR18","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.neunet.2020.07.025","volume":"131","author":"C Tian","year":"2020","unstructured":"Tian, C., Fei, L., Zheng, W., et al.: Deep learning on image denoising: an overview. Neural Netw. 131, 251\u2013275 (2020)","journal-title":"Neural Netw."},{"key":"3288_CR19","doi-asserted-by":"crossref","unstructured":"Tang, X., Zhao, X., Liu, J., et\u00a0al.: Uncertainty-aware unsupervised image deblurring with deep residual prior. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 9883\u20139892 (2023)","DOI":"10.1109\/CVPR52729.2023.00953"},{"key":"3288_CR20","doi-asserted-by":"crossref","unstructured":"Li, Z., Jiang, H., Zheng, Y.: Polarized color image denoising using pocoformer. arXiv:2207.00215 (2022)","DOI":"10.1109\/CVPR52729.2023.00952"},{"key":"3288_CR21","doi-asserted-by":"crossref","unstructured":"Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: 2008 IEEE conference on computer vision and pattern recognition, pp 1\u20138. IEEE(2008)","DOI":"10.1109\/CVPR.2008.4587597"},{"issue":"1","key":"3288_CR22","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/s11760-023-02728-3","volume":"18","author":"Z Hong","year":"2024","unstructured":"Hong, Z., Hamdan, E., Zhao, Y., et al.: Wildfire detection via transfer learning: a survey. SIViP 18(1), 207\u2013214 (2024)","journal-title":"SIViP"},{"key":"3288_CR23","unstructured":"Chen, T.H., Wu, P.H., Chiou, Y.C.: An early fire-detection method based on image processing. In: 2004 international conference on image processing, 2004. ICIP\u201904, pp. 1707\u20131710. IEEE (2004)"},{"issue":"4","key":"3288_CR24","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1007\/s11760-019-01600-7","volume":"14","author":"H Pan","year":"2020","unstructured":"Pan, H., Badawi, D., Zhang, X., et al.: Additive neural network for forest fire detection. SIViP 14(4), 675\u2013682 (2020)","journal-title":"SIViP"},{"issue":"1","key":"3288_CR25","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.patrec.2005.06.015","volume":"27","author":"BU T\u00f6reyin","year":"2006","unstructured":"T\u00f6reyin, B.U., Dedeo\u011flu, Y., G\u00fcd\u00fckbay, U., et al.: Computer vision based method for real-time fire and flame detection. Pattern Recogn. Lett. 27(1), 49\u201358 (2006)","journal-title":"Pattern Recogn. Lett."},{"issue":"2","key":"3288_CR26","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1109\/TCSVT.2014.2339592","volume":"25","author":"K Dimitropoulos","year":"2014","unstructured":"Dimitropoulos, K., Barmpoutis, P., Grammalidis, N.: Spatio-temporal flame modeling and dynamic texture analysis for automatic video-based fire detection. IEEE Trans. Circuits Syst. Video Technol. 25(2), 339\u2013351 (2014)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"3288_CR27","doi-asserted-by":"crossref","unstructured":"G\u00fcnay, O., \u00c7etin, A.E.: Real-time dynamic texture recognition using random sampling and dimension reduction. In: 2015 IEEE international conference on image processing (ICIP), pp. 3087\u20133091. IEEE (2015)","DOI":"10.1109\/ICIP.2015.7351371"},{"key":"3288_CR28","doi-asserted-by":"crossref","unstructured":"Aslan, S., G\u00fcd\u00fckbay, U., T\u00f6reyin, B.U., et al.: Early wildfire smoke detection based on motion-based geometric image transformation and deep convolutional generative adversarial networks. In: ICASSP 2019\u20132019 IEEE international conference on acoustics, Speech and Signal Processing (ICASSP), pp. 8315\u20138319. IEEE (2019)","DOI":"10.1109\/ICASSP.2019.8683629"},{"issue":"9","key":"3288_CR29","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He, K., Zhang, X., Ren, S., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904\u20131916 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3288_CR30","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., et\u00a0al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"3288_CR31","unstructured":"Ren, S., He, K., Girshick, R., et\u00a0al.: Faster r-cnn: Towards real-time object detection with region proposal networks. Adv. Neural Inform. Process. Syst. 28 (2015)"},{"key":"3288_CR32","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., et\u00a0al.: Ssd: Single shot multibox detector. In: Computer Vision\u2013ECCV 2016: 14th European conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14, pp. 21\u201337. Springer (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"3288_CR33","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., et\u00a0al.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"3288_CR34","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., et\u00a0al.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"3288_CR35","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7263\u20137271 (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"3288_CR36","unstructured":"Joseph, R., Ali, F., et\u00a0al.: Yolov3: an incremental improvement. arXiv:1804.02767 (2018)"},{"key":"3288_CR37","unstructured":"Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: optimal speed and accuracy of object detection. arXiv:2004.10934 (2020)"},{"key":"3288_CR38","doi-asserted-by":"publisher","unstructured":"Ultralytics (2022) ultralytics\/yolov5: v7.0 - YOLOv5 SOTA realtime instance segmentation. https:\/\/doi.org\/10.5281\/zenodo.7347926, Accessed 7 May (2023)","DOI":"10.5281\/zenodo.7347926"},{"key":"3288_CR39","unstructured":"Li, C., Li, L., Jiang, H., et\u00a0al.: Yolov6: a single-stage object detection framework for industrial applications. arXiv:2209.02976 (2022)"},{"key":"3288_CR40","doi-asserted-by":"publisher","first-page":"15075","DOI":"10.1007\/s11042-017-5090-2","volume":"77","author":"Y Luo","year":"2018","unstructured":"Luo, Y., Zhao, L., Liu, P., et al.: Fire smoke detection algorithm based on motion characteristic and convolutional neural networks. Multimed. Tools Appl. 77, 15075\u201315092 (2018)","journal-title":"Multimed. Tools Appl."},{"key":"3288_CR41","doi-asserted-by":"publisher","first-page":"58923","DOI":"10.1109\/ACCESS.2020.2982994","volume":"8","author":"C Chaoxia","year":"2020","unstructured":"Chaoxia, C., Shang, W., Zhang, F.: Information-guided flame detection based on faster r-cnn. IEEE Access 8, 58923\u201358932 (2020)","journal-title":"IEEE Access"},{"key":"3288_CR42","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 7464\u20137475 (2023)","DOI":"10.1109\/CVPR52729.2023.00721"},{"issue":"10","key":"3288_CR43","doi-asserted-by":"publisher","first-page":"8257","DOI":"10.1109\/TGRS.2020.3042507","volume":"59","author":"L Fu","year":"2020","unstructured":"Fu, L., Zhang, D., Ye, Q.: Recurrent thrifty attention network for remote sensing scene recognition. IEEE Trans. Geosci. Remote Sens. 59(10), 8257\u20138268 (2020)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"3288_CR44","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Liao, H.Y.M., Wu, Y.H., et\u00a0al.: Cspnet: a new backbone that can enhance learning capability of cnn. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops, pp. 390\u2013391 (2020)","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"3288_CR45","unstructured":"Jocher, G., Chaurasia, A., Qiu, J.: Ultralytics YOLO. https:\/\/github.com\/ultralytics\/ultralytics (2023)"},{"key":"3288_CR46","doi-asserted-by":"crossref","unstructured":"Nascimento, M.G.d., Fawcett, R., Prisacariu, V.A.: Dsconv: Efficient convolution operator. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 5148\u20135157 (2019)","DOI":"10.1109\/ICCV.2019.00525"},{"key":"3288_CR47","doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade r-cnn: delving into high quality object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6154\u20136162 (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"3288_CR48","unstructured":"Wang, C.Y., Yeh, I.H., Liao, H.Y.M.: You only learn one representation: unified network for multiple tasks. arXiv:2105.04206 (2021)"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03288-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-024-03288-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03288-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:17:44Z","timestamp":1732148264000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-024-03288-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,3]]},"references-count":48,"journal-issue":{"issue":"8-9","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["3288"],"URL":"https:\/\/doi.org\/10.1007\/s11760-024-03288-w","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,3]]},"assertion":[{"value":"1 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2024","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":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}