Enhanced Wildfire Detection in Forest Areas via OPENCV with Deep Convolutional Neural Networks for Smoke and Flame Segmentation

Forest fire detection is a critical component of environmental management systems, enabling early identification and response to mitigate devastating impacts on ecosystems, infrastructure, and human lives. A system utilizing OpenCV-based algorithms for real-time detection of forest fires. By employing HSV color space for segmentation and deep learning techniques, the proposed system achieves robust identification of fire regions. The Fmeasures are used to assess performance, showing its effectiveness. Enhancing such systems can significantly reduce fire- related losses globally. In difficult situations like dense smoke or at night, integrating multi-sensor data fusion—for example, merging satellite and thermal imaging data—can improve detection accuracy even more. Furthermore, by integrating real-time alarm systems with GIS mapping, authorities may more effectively deploy resources and pinpoint fire breakouts, cutting down on reaction times and minimizing fire-related losses worldwide.