An Efficient Wildfire Detection Model using the Novel Reduce-VGGNet and VGG19 Architectures

Wildfires pose significant ecological, economic, and social challenges worldwide, and early detection is crucial for minimizing their impact. Traditional methods of wildfire detection, such as manual monitoring and basic algorithms, often suffer from inefficiencies and delayed responses. This research proposes a deep learning-based framework for automated forest wildfire detection, integrating machine vision and image processing techniques. We introduce Reduce-VGGNet, an optimized version of the VGG16 model, which incorporates spatial and temporal feature extraction to enhance the accuracy and real-time detection capabilities. The system achieves an accuracy of 97% in wildfire image classification and region annotation. Furthermore, the model's performance is extended using VGG19, which increases accuracy to 99.68%. The proposed framework is evaluated using the FLAMES dataset, which includes both fire and non-fire images. Results demonstrate robust real-time detection on static images and video feeds, validated through metrics such as precision, recall, and F1-score. The system significantly outperforms traditional approaches like SVM and more advanced models like RCNN, offering a scalable and efficient solution for wildfire detection and prevention. This work bridges the gap between research and practical application, providing a valuable tool for wildfire management and disaster response.