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

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.