Real-Time Detection of Forest Fires Using FireNet-CNN and Explainable AI Techniques

This study presents FireNet-CNN, an advanced deep-learning model particularly designed for forest fire detection, which significantly surpasses existing methods in terms of reliability, efficiency, and interpretability. FireNet-CNN is compared to popular pre-trained models, including VGG16, VGG19, and Inception V3, across key performance metrics and consistently shows superior results, achieving 99.05% accuracy, 99.41% precision, and 98.28% recall. The model was evaluated using two augmented datasets: Dataset A and Dataset B, which consist of fire and non-fire images sourced from multiple video and image datasets. FireNet-CNN’s architecture, which includes 2.75 million parameters and a compact model size of 10.58 MB, has been meticulously optimized for fire detection tasks. As a consequence, the inference time of 0.95 seconds/image enables fast real-time deployment especially suitable for resource-constrained platforms like drones, remote sensors or other types of embedded systems in wooded regions. FireNet-CNN uses synthetic data augmentation based on Stable Diffusion to overcome the limitations of dataset size and class imbalance. This augmentation is critical as it helps the model accurately identify fire instances with a lower false positive rate, which is key for any real-time fire detection system where reliability and dependability are vital. To improve transparency and trust in safety-critical applications, FireNet-CNN incorporates the explainable AI (XAI) techniques, such as Grad-CAM and Saliency Maps. Despite encountering challenges such as reliance on synthetic data and issues of class imbalance, FireNet-CNN has demonstrated promising potential as a viable and effective solution for early wildfire detection. It offers significant insights for future research and practical applications in fire management and disaster response.