{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:25:32Z","timestamp":1760145932412,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62202248"],"award-info":[{"award-number":["62202248"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Fire detection is crucial due to the exorbitant annual toll on both human lives and the economy resulting from fire-related incidents. To enhance forest fire detection in complex environments, we propose a new algorithm called FD-Net for various environments. Firstly, to improve detection performance, we introduce a Fire Attention (FA) mechanism that utilizes the position information from feature maps. Secondly, to prevent geometric distortion during image cropping, we propose a Three-Scale Pooling (TSP) module. Lastly, we fine-tune the YOLOv5 network and incorporate a new Fire Fusion (FF) module to enhance the network\u2019s precision in identifying fire targets. Through qualitative and quantitative comparisons, we found that FD-Net outperforms current state-of-the-art algorithms in performance on both fire and fire-and-smoke datasets. This further demonstrates FD-Net\u2019s effectiveness for application in fire detection.<\/jats:p>","DOI":"10.3390\/rs16183382","type":"journal-article","created":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T02:30:23Z","timestamp":1726108223000},"page":"3382","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FD-Net: A Single-Stage Fire Detection Framework for Remote Sensing in Complex Environments"],"prefix":"10.3390","volume":"16","author":[{"given":"Jianye","family":"Yuan","sequence":"first","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 473072, China"}]},{"given":"Haofei","family":"Wang","sequence":"additional","affiliation":[{"name":"Peng Cheng Laboratory, Department of Mathematics and Theories, Shenzhen 518000, China"}]},{"given":"Minghao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Xiaohan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Weiwei","family":"Song","sequence":"additional","affiliation":[{"name":"Peng Cheng Laboratory, Department of Mathematics and Theories, Shenzhen 518000, China"}]},{"given":"Song","family":"Li","sequence":"additional","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 473072, China"}]},{"given":"Wei","family":"Gong","sequence":"additional","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 473072, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yang, H., Wang, J., and Wang, J. (2023). Efficient Detection of Forest Fire Smoke in UAV Aerial Imagery Based on an Improved YOLOv5 Model and Transfer Learning. Remote Sens., 15.","DOI":"10.3390\/rs15235527"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yuan, J., Ma, X., and Han, G. (2022). Research on Lightweight Disaster Classification Based on High-Resolution Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14112577"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.neucom.2021.09.026","article-title":"Global2Salient: Self-adaptive feature aggregation for remote sensing smoke detection","volume":"466","author":"Chen","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.1016\/j.media.2021.102035","article-title":"Loss odyssey in medical image segmentation","volume":"71","author":"Ma","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.ins.2021.07.091","article-title":"TWD-SFNN: Three-way decisions with a single hidden layer feedforward neural network","volume":"579","author":"Cheng","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/978-981-33-6919-1_4","article-title":"Comparison and Analysis of RNN-LSTMs and CNNs for Social Reviews Classification","volume":"1319","author":"Bodapati","year":"2021","journal-title":"Adv. Intell. Syst. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Abdel-Magied, M.F., Loparo, K.A., and Lin, W. (1998, January 26). Fault detection and diagnosis for rotating machinery: A model-based approach. Proceedings of the 1998 American Control Conference, Philadelphia, PA, USA.","DOI":"10.1109\/ACC.1998.703183"},{"key":"ref_8","unstructured":"Bochkovskiy, A., Wang, C., and Liao, H.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lyu, S., Wang, X., and Zhao, Q. (2021, January 11\u201317). TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, W., and Fu, C.-Y. (2016, January 11\u201314). SSD: Single Shot MultiBox Detector. Proceedings of the Computer Vision\u2014ECCV, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"120465","DOI":"10.1016\/j.eswa.2023.120465","article-title":"A modified YOLOv5 architecture for efficient fire detection in smart cities","volume":"231","author":"Yar","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.comcom.2023.08.020","article-title":"An IoT based forest fire detection system using integration of cat swarm with LSTM model","volume":"211","author":"Mahaveerakannan","year":"2023","journal-title":"Comput. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2233","DOI":"10.1016\/j.procs.2023.01.199","article-title":"FireNet-v2: Improved Lightweight Fire Detection Model for Real-Time IoT Applications","volume":"218","author":"Shees","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_15","unstructured":"Jadon, A., Omama, M., and Varshney, A. (2019). FireNet: A specialized lightweight fire & smoke detection model for real-time IoT applications. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jiang, H., and Learned-Miller, E. (June, January 30). Face detection with the faster R-CNN. Proceedings of the 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), Washington, DC, USA.","DOI":"10.1109\/FG.2017.82"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bharati, P., and Pramanik, A. (2020). Deep learning techniques\u2014R-CNN to mask R-CNN: A survey. Computational Intelligence in Pattern Recognition, Springer.","DOI":"10.1007\/978-981-13-9042-5_56"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018, January 18\u201322). Cascade R-CNN: Delving into High Quality Object Detection. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"119747","DOI":"10.1016\/j.eswa.2023.119747","article-title":"EdgeFireSmoke++: A novel lightweight algorithm for real-time forest fire detection and visualization using internet of things-human machine interface","volume":"221","author":"Almeida","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Pritam, D., and Dewan, J.H. (2017, January 7\u20139). Detection of fire using image processing techniques with LUV color space. Proceedings of the 2017 2nd International Conference for Convergence in Technology (I2CT), Mumbai, India.","DOI":"10.1109\/I2CT.2017.8226309"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1007\/s11263-019-01204-1","article-title":"CornerNet: Detecting Objects as Paired Keypoints","volume":"128","author":"Law","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_22","unstructured":"Zhou, X., Wang, D., and Kr\u00e4henb\u00fchl, P. (2019). Objects as points. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1016\/j.buildenv.2009.10.017","article-title":"Multi-feature fusion based fast video flame detection","volume":"45","author":"Chen","year":"2010","journal-title":"Build. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.eswa.2016.09.021","article-title":"A new PSO-based approach to fire flame detection using K-Medoids clustering","volume":"68","author":"Khatami","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, K., Cheng, Y., Bai, H., Mou, C., and Zhang, Y. (2019, January 18\u201320). Research on Image Fire Detection Based on Support Vector Machine. Proceedings of the 2019 9th International Conference on Fire Science and Fire Protection Engineering (ICFSFPE), Chengdu, China.","DOI":"10.1109\/ICFSFPE48751.2019.9055795"},{"key":"ref_27","unstructured":"Xia, D., and Wang, S. (2006, January 21\u201323). Research on Detection Method of Uncertainty Fire Signal Based on Fire Scenario. Proceedings of the 2006 6th World Congress on Intelligent Control and Automation, Dalian, China."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate Attention for Efficient Mobile Network Design. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1007\/s10489-021-02496-y","article-title":"DECA: A novel multi-scale efficient channel attention module for object detection in real-life fire images","volume":"52","author":"Wang","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., and Lee, J.Y. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the Computer Vision\u2014ECCV 2018, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_32","first-page":"543","article-title":"A novel approach based on the fast sigmoid function for interpretation of potential field data","volume":"62","author":"Oksum","year":"2021","journal-title":"Bull. Geophys. Oceanogr."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"107456","DOI":"10.1016\/j.knosys.2021.107456","article-title":"MC-Net: Multiple max-pooling integration module and cross multi-scale deconvolution network","volume":"231","author":"You","year":"2021","journal-title":"Knowl. -Based Syst."},{"key":"ref_34","first-page":"2797","article-title":"Deep Rank-Based Average Pooling Network for COVID-19 Recognition","volume":"70","author":"Wang","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s00138-020-01128-8","article-title":"A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis","volume":"32","author":"Zhang","year":"2021","journal-title":"Mach. Vis. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4236\/jcc.2021.912001","article-title":"Activation Function: Cell Recognition Based on yolov5s\/m","volume":"9","author":"Yang","year":"2021","journal-title":"J. Comput. Commun."},{"key":"ref_37","unstructured":"Kingma, D.P., and Ba, J.A. (2020). A method for stochastic optimization, arXiv 2014. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1021\/ac901753c","article-title":"Use of the hue parameter of the hue, saturation, value color space as a quantitative analytical parameter for bitonal optical sensors","volume":"82","author":"Cantrell","year":"2010","journal-title":"Anal. Chem."},{"key":"ref_39","first-page":"489","article-title":"Affine transformation resistant watermarking based on image normalization","volume":"3","author":"Dong","year":"2002","journal-title":"Int. Conf. Image Process."},{"key":"ref_40","first-page":"6438","article-title":"Manifold Mixup: Better Representations by Interpolating Hidden States","volume":"97","author":"Verma","year":"2018","journal-title":"Int. Conf. Mach. Learn."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., and Yoo, Y. (November, January 27). CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref_42","unstructured":"Zhong, Z., Zheng, L., and Kang, G. (2020, January 7\u201312). Random erasing data augmentation. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Jacksi, K., Ibrahim, R.K., Zeebaree, S.R.M., Zebari, R.R., and Sadeeq, M.A.M. (2020, January 23\u201324). Clustering Documents Based on Semantic Similarity Using HAC and K-Mean Algorithms. Proceedings of the 2020 International Conference on Advanced Science and Engineering (ICOASE), Duhok, Iraq.","DOI":"10.1109\/ICOASE51841.2020.9436570"},{"key":"ref_44","unstructured":"Redmon, J., and Farhadi, A. (2018). yolov3: An incremental improvement. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2021, January 20\u201325). Scaled-YOLOv4: Scaling Cross Stage Partial Network. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01283"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_47","unstructured":"Ge, Z., Liu, S., and Wang, F. (2021). Yolox: Exceeding yolo series in 2021. arXiv."},{"key":"ref_48","unstructured":"Wang, C.Y., Yeh, I.H., and Liao, H.Y. (2024). YOLOv9: Learning what you want to learn using programmable gradient information. arXiv."},{"key":"ref_49","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., and Ding, G. (2024). Yolov10: Real-time end-to-end object detection. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1820","DOI":"10.1109\/TCSVT.2021.3083112","article-title":"EFFNet: Enhanced Feature Foreground Network for Video Smoke Source Prediction and Detection","volume":"32","author":"Cao","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3382\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:54:11Z","timestamp":1760111651000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3382"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,11]]},"references-count":50,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16183382"],"URL":"https:\/\/doi.org\/10.3390\/rs16183382","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,9,11]]}}}