Adaptive Hierarchical Multi-Headed Convolutional Neural Network With Modified Convolutional Block Attention for Aerial Forest Fire Detection

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dc.authoridHo, Ivan Wang-Hei/0000-0003-0043-2025
dc.authoridMasum, Shamsul/0000-0001-8489-9356
dc.authoridMowla, Md. Najmul/0000-0003-0613-9858
dc.contributor.authorMowla, Md. Najmul
dc.contributor.authorAsadi, Davood
dc.contributor.authorMasum, Shamsul
dc.contributor.authorRabie, Khaled
dc.date.accessioned2025-04-09T12:32:04Z
dc.date.available2025-04-09T12:32:04Z
dc.date.issued2025
dc.description.abstractEffective detection and classification of forest fire imagery are critical for timely and efficient wildfire management. Convolutional Neural Networks (CNNs) have demonstrated potential in this domain but encounter limitations when addressing varying scales, resolutions, and complex spatial dependencies inherent in wildfire datasets. Building upon our prior work on the Unmanned Aerial Vehicle-based Forest Fire Database (UAVs-FFDB) and the multi-headed CNN (MHCNN), this study introduces a novel architecture, namely, the Adaptive Hierarchical Multi-Headed Convolutional Neural Network with Modified Convolutional Block Attention Module (AHMHCNN-mCBAM). This enhanced framework addresses prior challenges by integrating adaptive pooling, concatenated convolutions for multi-scale feature extraction, and an improved attention mechanism incorporating shared fully connected layers, Glorot initialization, rectified linear units (ReLU), layer normalization, and attention-gating. AHMHCNN-mCBAM incorporates Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM) networks for temporal context modeling to further refine classification accuracy. Experiments conducted on the UAVs-FFDB dataset achieved outstanding results, including 100% accuracy, a 100% Cohen's kappa coefficient (cKappa), and compact model parameter sizes of 1.49 million (M), 0.25 M, and 0.12 M. On the Fire Luminosity Airborne-based Machine Learning Evaluation (FLAME) dataset, the model attained accuracy rates of 99.83%, 99.10%, and 99.32%, with corresponding cKappa values of 99.66%, 98.20%, and 98.65%. Compared to the baseline hierarchical MHCNN with CBAM (HMHCNN-CBAM), AHMHCNN-mCBAM demonstrated significant performance gains, including a 6.80% and 6.59% increase in accuracy, a 9.26% and 14.11% improvement in cKappa, and a 13.87% and 13.76% reduction in parameter size on the UAVs-FFDB and FLAME datasets, respectively. Additionally, AHMHCNN-mCBAM outperformed HMHCNN-CBAM in recall (25% improvement), precision (21.95%), F1-score (14.94%), and fire detection rate (FDR) reduction (25.01%), while achieving a 100% reduction in error warning rate (EWR). Leveraging Explainable Artificial Intelligence (XAI) techniques, the model provides interpretable insights into decision-making processes.
dc.identifier.doi10.1109/ACCESS.2024.3524320
dc.identifier.endpage3433
dc.identifier.issn2169-3536
dc.identifier.startpage3412
dc.identifier.urihttp://dx.doi.org/10.1109/ACCESS.2024.3524320
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4299
dc.identifier.volume13
dc.identifier.wosWOS:001394723100045
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherIEEE-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIEEE Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20250370
dc.subjectForestry
dc.subjectFeature extraction
dc.subjectAccuracy
dc.subjectFires
dc.subjectConvolutional neural networks
dc.subjectAttention mechanisms
dc.subjectAdaptation models
dc.subjectTraining
dc.subjectVehicle dynamics
dc.subjectAutonomous aerial vehicles
dc.subjectAdaptive hierarchical convolutional network
dc.subjectmodified convolutional block attention module
dc.subjectunmanned aerial vehicle
dc.subjectforest fire detection
dc.titleAdaptive Hierarchical Multi-Headed Convolutional Neural Network With Modified Convolutional Block Attention for Aerial Forest Fire Detection
dc.typeArticle

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