{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T04:15:07Z","timestamp":1773807307965,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for the Central Universities","award":["QNTD202304"],"award-info":[{"award-number":["QNTD202304"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2023XAGG0065"],"award-info":[{"award-number":["2023XAGG0065"]}]},{"name":"Xiong\u2019an New Area Science and Technology Innovation Special Project of Ministry of Science and Technology of China","award":["QNTD202304"],"award-info":[{"award-number":["QNTD202304"]}]},{"name":"Xiong\u2019an New Area Science and Technology Innovation Special Project of Ministry of Science and Technology of China","award":["2023XAGG0065"],"award-info":[{"award-number":["2023XAGG0065"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Early wildfire smoke detection faces challenges such as limited datasets, small target sizes, and interference from smoke-like objects. To address these issues, we propose a novel approach leveraging Efficient Channel and Dilated Convolution Spatial Attention (EDA). Specifically, we develop an experimental dataset, Smoke-Exp, consisting of 6016 images, including real-world and Cycle-GAN-generated synthetic wildfire smoke images. Additionally, we introduce M-YOLO, an enhanced YOLOv5-based model with a 4\u00d7 downsampling detection head, and MEDA-YOLO, which incorporates the EDA mechanism to filter irrelevant information and suppress interference. Experimental results on Smoke-Exp demonstrate that M-YOLO achieves a mean Average Precision (mAP) of 96.74%, outperforming YOLOv5 and Faster R-CNN by 1.32% and 3.26%, respectively. MEDA-YOLO further improves performance, achieving an mAP of 97.58%, a 2.16% increase over YOLOv5. These results highlight the potential of the proposed models for precise and real-time early wildfire smoke detection.<\/jats:p>","DOI":"10.3390\/rs16244684","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T10:08:53Z","timestamp":1734343733000},"page":"4684","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Early Wildfire Smoke Detection Method Based on EDA"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7052-4887","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"},{"name":"State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China"},{"name":"Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China"}]},{"given":"Faying","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"},{"name":"State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China"},{"name":"Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4014-2455","authenticated-orcid":false,"given":"Changchun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"},{"name":"State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China"},{"name":"Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China"}]},{"given":"Yuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"},{"name":"State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China"},{"name":"Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2267-786X","authenticated-orcid":false,"given":"Junguo","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"},{"name":"State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China"},{"name":"Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,15]]},"reference":[{"key":"ref_1","first-page":"WF23044","article-title":"LEF-YOLO: A lightweight method for intelligent detection of four extreme wildfires based on the YOLO framework","volume":"33","author":"Li","year":"2024","journal-title":"Int. J. Wildland Fire"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"82095","DOI":"10.1109\/ACCESS.2024.3406215","article-title":"Wildfire Detection with Deep Learning\u2014A Case Study for the CICLOPE Project","volume":"12","author":"Ferreira","year":"2024","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Al-Smadi, Y., Alauthman, M., Al-Qerem, A., Aldweesh, A., Quaddoura, R., Aburub, F., Mansour, K., and Alhmiedat, T. (2023). Early Wildfire Smoke Detection Using Different YOLO Models. Machines, 11.","DOI":"10.3390\/machines11020246"},{"key":"ref_4","first-page":"705","article-title":"A Review of Global Forest Fires in 2021","volume":"41","author":"Bai","year":"2022","journal-title":"Fire Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mohapatra, A., and Trinh, T. (2022). Early Wildfire Detection Technologies in Practice\u2014A Review. Sustainability, 14.","DOI":"10.3390\/su141912270"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.11834\/jig.190230","article-title":"From traditional methods to deep ones: Review of visual smoke recognition, detection, and segmentation","volume":"24","author":"Xia","year":"2019","journal-title":"J. Image Graph."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Gui, S., Song, S., Qin, R., and Tang, Y. (2024). Remote Sensing Object Detection in the Deep Learning Era\u2014A Review. Remote Sens., 16.","DOI":"10.3390\/rs16020327"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Maillard, S., Safayet Khan, M., Cramer, A., and Karanci Sancar, E. (2024, January 13\u201314). Wildfire and Smoke Detection Using YOLO-NAS. Proceedings of the 2024 IEEE 3rd International Conference on Computing and Machine Intelligence (ICMI), Mt Pleasant, MI, USA.","DOI":"10.1109\/ICMI60790.2024.10585773"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"El-Madafri, I., Pe\u00f1a, M., and Olmedo-Torre, N. (2024). Real-Time Forest Fire Detection with Lightweight CNN Using Hierarchical Multi-Task Knowledge Distillation. Fire, 7.","DOI":"10.3390\/fire7110392"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, M., Yue, P., Jiang, L., Yu, D., Tuo, T., and Li, J. (2024). An Open Flame and Smoke Detection Dataset for Deep Learning in Remote Sensing based Fire Detection. Geo-Spat. Inf. Sci., 1\u201316.","DOI":"10.1080\/10095020.2024.2347922"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, T., Zhao, E., Zhang, J., and Hu, C. (2019). Detection of Wildfire Smoke Images Based on a Densely Dilated Convolutional Network. Electronics, 8.","DOI":"10.3390\/electronics8101131"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Srinivas, P., Maheshwari, U., Jha, S.K., Bajpai, I., and Lalmohan, K.S. (2024, January 27\u201329). Smart Early Fire and Smoke Detection with Transfer Learning. Proceedings of the 2024 IEEE Region 10 Symposium (TENSYMP), New Delhi, India.","DOI":"10.1109\/TENSYMP61132.2024.10752273"},{"key":"ref_13","first-page":"4708523","article-title":"RFWNet: A Multiscale Remote Sensing Forest Wildfire Detection Network With Digital Twinning, Adaptive Spatial Aggregation, and Dynamic Sparse Features","volume":"62","author":"Wang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108977","DOI":"10.1016\/j.engappai.2024.108977","article-title":"Wildfire and smoke early detection for drone applications: A light-weight deep learning approach","volume":"136","author":"Kumar","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_15","unstructured":"Liu, Y., Chen, F., Zhang, C., Zhang, J., and Wang, Y. (2024, January 19\u201321). Early Wildfire Smoke Detection Method Based on EDA. Proceedings of the 5th China Computer Application Conference on Forestry and Grassland (CACFG), Kunming, China."},{"key":"ref_16","first-page":"242","article-title":"Research on Monitoring Technology of Mountain Fire Disaster Based on UAV\u2019s Power Transmission Line","volume":"04","author":"Chen","year":"2019","journal-title":"Autom. Instrum."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"9856739","DOI":"10.34133\/2022\/9856739","article-title":"Shortwave Radiation Calculation for Forest Plots Using Airborne LiDAR Data and Computer Graphics","volume":"2022","author":"Xue","year":"2022","journal-title":"Plant Phenomics"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mok, T.C.W., Li, Z., Bai, Y., Zhang, J., Liu, W., Zhou, Y., Yan, K., Jin, D., Shi, Y., and Yin, X. (2024, January 19). Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration. Proceedings of the 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01066"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Alotaibi, A., Alatawi, H., Binnouh, A., Duwayriat, L., Alhmiedat, T., and Alia, O.M. (2024). Deep Learning-Based Vision Systems for Robot Semantic Navigation: An Experimental Study. Technologies, 12.","DOI":"10.3390\/technologies12090157"},{"key":"ref_20","first-page":"180","article-title":"Deep Learning-based Multimodal Trajectory Prediction Methods for Autonomous Driving: State of the Art and Perspectives","volume":"5","author":"Huang","year":"2023","journal-title":"Chin. J. Intell. Sci. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Seralathan, P., and Edward, S. (2024, January 18). Revolutionizing Agriculture: Deep Learning-Based Crop Monitoring Using UAV Imagery\u2014A Review. Proceedings of the 2024 International Conference on Expert Clouds and Applications (ICOECA), Bengaluru, India.","DOI":"10.1109\/ICOECA62351.2024.00139"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"106390","DOI":"10.1016\/j.engappai.2023.106390","article-title":"IDD-Net: Industrial Defect Detection Method Based on Deep-Learning","volume":"123","author":"Zhang","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"102705","DOI":"10.1016\/j.ecoinf.2024.102705","article-title":"Adaptive Image Processing Embedding to Make the Ecological Tasks of Deep Learning More Robust on Camera Traps Images","volume":"82","author":"Yang","year":"2024","journal-title":"Ecol. Inform."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zahrawi, M., and Shaalan, K. (2023). Improving Video Surveillance Systems in Banks Using Deep Learning Techniques. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-35190-9"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.1007\/s11676-020-01230-7","article-title":"Forest Fire Smoke Recognition Based on Convolutional Neural Network","volume":"32","author":"Sun","year":"2021","journal-title":"J. For. Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Shang, D., Zhang, F., Yuan, D., Hong, L., Zheng, H., and Yang, F. (2024). Deep Learning-based Forest Fire Risk Research on Monitoring and Early Warning Algorithms. Fire, 7.","DOI":"10.3390\/fire7040151"},{"key":"ref_27","unstructured":"Mohnish, S., Akshay, K.P., Gokul Ram, S., Sarath Vignesh, A., Pavithra, P., and Ezhilarasi, S. (2022, January 12). Deep Learning Based Forest Fire Detection and Alert System. Proceedings of the 2022 International Conference on Communication (ICC), Chennai, India."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Park, G., and Lee, Y. (2024). Wildfire Smoke Detection Enhanced by Image Augmentation with StyleGAN2-ADA for YOLOv8 and RT-DETR Models. Fire, 7.","DOI":"10.3390\/fire7100369"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"29471","DOI":"10.1109\/ACCESS.2019.2902606","article-title":"Adversarial Adaptation from Synthesis to Reality in Fast Detector for Smoke Detection","volume":"7","author":"Xu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Krichen, M. (2023, January 6\u20138). Generative Adversarial Networks. Proceedings of the 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India.","DOI":"10.1109\/ICCCNT56998.2023.10306417"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1109\/TIFS.2023.3328431","article-title":"ResNeXt Plus: Attention Mechanisms Based on ResNeXt for Malware Detection and Classification","volume":"19","author":"He","year":"2023","journal-title":"Leee Trans. Inf. Forensics Secur."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"33193","DOI":"10.1109\/JIOT.2024.3426512","article-title":"ASOD: An Atrous Object Detection Model Using Multiple Attention Mechanisms for Obstacle Detection in Intelligent Connected Vehicles","volume":"11","author":"Li","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yang, S., Liu, Y., Liu, Z., Xu, C., and Du, X. (2024). Enhanced Vehicle Logo Detection Method Based on Self-Attention Mechanism for Electric Vehicle Application. World Electr. Veh. J., 15.","DOI":"10.20944\/preprints202409.0558.v1"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e37470","DOI":"10.1016\/j.heliyon.2024.e37470","article-title":"Remote Sensing Image Road Network Detection Based on Channel Attention Mechanism","volume":"10","author":"Shan","year":"2024","journal-title":"Heliyon"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"108785","DOI":"10.1016\/j.patcog.2022.108785","article-title":"HAM: Hybrid attention module in deep convolutional neural networks for image classification","volume":"129","author":"Li","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s00466-023-02434-4","article-title":"Deep Learning in Computational Mechanics: A Review","volume":"74","author":"Herrmann","year":"2024","journal-title":"Comput. Mech."},{"key":"ref_37","unstructured":"(2024, September 03). KMU Fire & Smoke Database. Available online: https:\/\/cvpr.kmu.ac.kr\/."},{"key":"ref_38","unstructured":"(2024, September 03). Sample Fire and Smoke Video Clips. Available online: http:\/\/signal.ee.bilkent.edu.tr\/VisiFire\/Demo\/SampleClips.html."},{"key":"ref_39","unstructured":"(2024, September 03). Feiniu\u2019s Homepage. Available online: http:\/\/staff.ustc.edu.cn\/~yfn\/index.html."},{"key":"ref_40","unstructured":"(2024, September 03). Research Webpage About Smoke Detection for Fire Alarm: Datasets. Available online: http:\/\/smoke.ustc.edu.cn\/datasets.htm."},{"key":"ref_41","first-page":"3041","article-title":"CycleGAN-based Data Enhancement Method for Lunar Surface Images","volume":"45","author":"Song","year":"2023","journal-title":"Syst. Eng. Electron."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhu, J., Park, T., Isola, P., and Efros, A.A. (2017, January 25). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_43","unstructured":"(2024, September 03). LabelImg PyPi. Available online: https:\/\/pypi.org\/project\/labelImg\/."},{"key":"ref_44","unstructured":"(2024, September 03). The PASCAL Visual Object Classes Homepage. Available online: http:\/\/host.robots.ox.ac.uk\/pascal\/VOC\/."},{"key":"ref_45","unstructured":"(2024, September 03). Ultralytics\/yolov5. Available online: https:\/\/github.com\/ultralytics\/yolov5\/."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhao, L., Zhi, L., Zhao, C., and Zheng, W. (2022). Fire-YOLO: A Small Target Object Detection Method for Fire Inspection. Sustainability, 14.","DOI":"10.3390\/su14094930"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lu, D., Cheng, S., Wang, L., and Song, S. (2022). Multi-scale Feature Progressive Fusion Network for Remote Sensing Image Change Detection. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-16329-6"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"12059","DOI":"10.1109\/ACCESS.2023.3241808","article-title":"YOLOv5-Based Model Integrating Separable Convolutions for Detection of Wheat Head Images","volume":"11","author":"Shen","year":"2023","journal-title":"IEEE Access"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13\u201319). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_51","first-page":"1491","article-title":"A Mult-task Traffic Scene Detection Model Based on Cross-attention","volume":"50","author":"Niu","year":"2023","journal-title":"J. Beijing Univ. Aeronaut. Astronaut."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wang, C., Bochkovskiy, A., and Liao, H.M. (2023, January 17\u201324). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_53","unstructured":"Rejin, V., and Sambath, M. (2024, January 18\u201319). YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness. Proceedings of the 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS), Chennai, India."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024, January 17\u201321). DETRs Beat YOLOs on Real-time Object Detection. Proceedings of the 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01605"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4684\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:52:40Z","timestamp":1760115160000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4684"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,15]]},"references-count":55,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16244684"],"URL":"https:\/\/doi.org\/10.3390\/rs16244684","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,15]]}}}