{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T23:54:20Z","timestamp":1783122860586,"version":"3.54.6"},"reference-count":30,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,15]],"date-time":"2022-01-15T00:00:00Z","timestamp":1642204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Forest fire detection from videos or images is vital to forest firefighting. Most deep learning based approaches rely on converging image loss, which ignores the content from different fire scenes. In fact, complex content of images always has higher entropy. From this perspective, we propose a novel feature entropy guided neural network for forest fire detection, which is used to balance the content complexity of different training samples. Specifically, a larger weight is given to the feature of the sample with a high entropy source when calculating the classification loss. In addition, we also propose a color attention neural network, which mainly consists of several repeated multiple-blocks of color-attention modules (MCM). Each MCM module can extract the color feature information of fire adequately. The experimental results show that the performance of our proposed method outperforms the state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/e24010128","type":"journal-article","created":{"date-parts":[[2022,1,16]],"date-time":"2022-01-16T20:44:00Z","timestamp":1642365840000},"page":"128","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Forest Fire Detection via Feature Entropy Guided Neural Network"],"prefix":"10.3390","volume":"24","author":[{"given":"Zhenwei","family":"Guan","sequence":"first","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Robot, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6846-8916","authenticated-orcid":false,"given":"Feng","family":"Min","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Robot, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9192-9728","authenticated-orcid":false,"given":"Wei","family":"He","sequence":"additional","affiliation":[{"name":"College of Physics and Electronic Information Engineering, Minjiang University, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhua","family":"Fang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Robot, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Lu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Robot, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,15]]},"reference":[{"key":"ref_1","unstructured":"Jadon, A., Omama, M., Varshney, A., Ansari, M.S., and Sharma, R. (2019). FireNet: A specialized lightweight fire & smoke detection model for real-time IoT applications. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s11760-014-0738-0","article-title":"Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the YCbCr color space","volume":"10","author":"Liu","year":"2016","journal-title":"Signal Image Video Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2019\/1939171","article-title":"A Real-Time Fire Detection Method from Video with Multifeature Fusion","volume":"2019","author":"Gong","year":"2019","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_5","first-page":"773","article-title":"A Flame Detection Method Based on Novel Gradient Features","volume":"29","author":"Zhu","year":"2018","journal-title":"J. Intell. Syst."},{"key":"ref_6","unstructured":"Chen, T.H., Wu, P.H., and Chiou, Y.C. (2004, January 24\u201327). An early fire-detection method based on image processing. Proceedings of the 2004 International Conference on Image Processing, ICIP \u201904, Singapore."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Rinsurongkawong, S., Ekpanyapong, M., and Dailey, M.N. (2012, January 16\u201318). Fire detection for early fire alarm based on optical flow video processing. Proceedings of the 2012 9th International Conference on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology, Phetchaburi, Thailand.","DOI":"10.1109\/ECTICon.2012.6254144"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Xu, J., Xu, L., and Guo, H. (2016, January 30\u201331). Deep convolutional neural networks for forest fire detection. Proceedings of the 2016 International Forum on Management, Education and Information Technology Application, Guangzhou, China.","DOI":"10.2991\/ifmeita-16.2016.105"},{"key":"ref_9","unstructured":"Sharma, J., Granmo, O.C., Goodwin, M., and Fidje, J.T. Deep convolutional neural networks for fire detection in images. Proceedings of the International Conference on Engineering Applications of Neural Networks."},{"key":"ref_10","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 21\u201326). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.neucom.2017.04.083","article-title":"Early fire detection using convolutional neural networks during surveillance for effective disaster management","volume":"288","author":"Muhammad","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"18174","DOI":"10.1109\/ACCESS.2018.2812835","article-title":"Convolutional Neural Networks based Fire Detection in Surveillance Videos","volume":"2018","author":"Muhammad","year":"2018","journal-title":"IEEE Access"},{"key":"ref_14","first-page":"1419","article-title":"Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications","volume":"49","author":"Khan","year":"2018","journal-title":"IEEE Trans. Syst. Man Cybernet. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhao, H., Kong, X., He, J., Qiao, Y., and Dong, C. (2020). Efficient image super-resolution using pixel attention. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-67070-2_3"},{"key":"ref_16","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 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path aggregation network for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_18","unstructured":"RangiLyu (2021, May 08). RangiLyu\/Nanodet. Available online: https:\/\/github.com\/RangiLyu\/nanodet."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., and Yang, J. (2020). Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. arXiv.","DOI":"10.1109\/CVPR46437.2021.01146"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_21","unstructured":"DeepQuestAI (2021, May 08). DeepQuestAI\/Fire-Smoke-Dataset. Available online: https:\/\/github.com\/DeepQuestAI\/Fire-Smoke-Dataset."},{"key":"ref_22","unstructured":"CAIR (2021, May 08). cair\/Fire-Detection-Image-Dataset. Available online: https:\/\/github.com\/cair\/Fire-Detection-Image-Dataset."},{"key":"ref_23","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, PMLR, Long Beach, CA, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014). Microsoft coco: Common objects in context. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_25","unstructured":"Ultralytics (2021, May 08). ultralytics\/yolov5. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_26","first-page":"91","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lu, X., Li, B., Yue, Y., Li, Q., and Yan, J. (2019, January 15\u201320). Grid r-cnn. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00754"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., and Li, S.Z. (2020, January 13\u201319). Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"ref_29","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"100625","DOI":"10.1016\/j.csite.2020.100625","article-title":"Image fire detection algorithms based on convolutional neural networks","volume":"19","author":"Li","year":"2020","journal-title":"Case Stud. Therm. Eng."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/1\/128\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:27:29Z","timestamp":1760362049000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/1\/128"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,15]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["e24010128"],"URL":"https:\/\/doi.org\/10.3390\/e24010128","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,15]]}}}