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<p>Early detection of forest fires is essential to limit ecological damage and economic loss. This study evaluates two lightweight convolutional models for binary fire recognition using a balanced dataset of 5121 annotated images spanning diverse environments and illumination conditions. The first model, Att-MobileNetV2, augments MobileNetV2 with a Convolutional Block Attention Module to prioritize informative spatial and channel responses. The second model, MobileNetV2-TL, adopts transfer learning by retaining pre-trained MobileNetV2 weights and training compact task-specific heads. On the held-out test set, Att-MobileNetV2 attains 99.61% accuracy with an F1-score of 99.70%, precision of 99.32%, and recall of 99.19%. MobileNetV2-TL achieves 98.42% accuracy, 98.43% F1-score, 98.42% precision, and 99.47% recall. Ablation results indicate that attention improves discriminability over the MobileNetV2 backbone, and attention heatmaps provide qualitative evidence of focus on flame regions. Comparisons with classical machine-learning pipelines (RFC, SVM) and CNN baselines (e.g., VGG16) under a unified preprocessing and training regimen show consistent improvements. Model size and computational load remain sufficiently low for real-time inference on resource-limited platforms, including UAVs and fixed cameras. The results indicate a favorable balance between accuracy and efficiency and point to practical deployment in continuous fire-monitoring settings.</p>

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Attention-enhanced MobileNetV2 models for robust forest fire detection and classification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月7日

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  • Ihtisham Ul Haq,
  • Ghassan Husnain,
  • Abid Iqbal,
  • Ali S. Alzahrani

摘要

Early detection of forest fires is essential to limit ecological damage and economic loss. This study evaluates two lightweight convolutional models for binary fire recognition using a balanced dataset of 5121 annotated images spanning diverse environments and illumination conditions. The first model, Att-MobileNetV2, augments MobileNetV2 with a Convolutional Block Attention Module to prioritize informative spatial and channel responses. The second model, MobileNetV2-TL, adopts transfer learning by retaining pre-trained MobileNetV2 weights and training compact task-specific heads. On the held-out test set, Att-MobileNetV2 attains 99.61% accuracy with an F1-score of 99.70%, precision of 99.32%, and recall of 99.19%. MobileNetV2-TL achieves 98.42% accuracy, 98.43% F1-score, 98.42% precision, and 99.47% recall. Ablation results indicate that attention improves discriminability over the MobileNetV2 backbone, and attention heatmaps provide qualitative evidence of focus on flame regions. Comparisons with classical machine-learning pipelines (RFC, SVM) and CNN baselines (e.g., VGG16) under a unified preprocessing and training regimen show consistent improvements. Model size and computational load remain sufficiently low for real-time inference on resource-limited platforms, including UAVs and fixed cameras. The results indicate a favorable balance between accuracy and efficiency and point to practical deployment in continuous fire-monitoring settings.