Wildfire Smoke Detection Using Faster R-CNN

Wildfires are one of the greatest dangerous and damaging natural catastrophes, triggering significant harm to property and posing a threat to life. Therefore, the detection and early warning of wildfires are critical to prevent their devastating consequences. In current years, deep learning-based object detection approaches such as Faster R-CNN have shown promising results in various applications, including wildfire detection. This paper proposes a wildfire detection system using Faster R-CNN, which receives satellite images as input and detects potential wildfires in real-time. The proposed system consists of two main mechanisms: a pre-processing module and a detection module. In the pre-processing module, the input satellite images are pre-processed, and the Region Proposal Network (RPN) makes candidate regions of interest (RoIs). The detection module then takes these RoIs as input and detects the presence of wildfire in each RoI using Faster R-CNN. To measure the result of the anticipated system, experiments were showed on a dataset consisting of images of forests and other regions. The outcomes established that the anticipated system can accomplish high correctness and efficiency in detecting wildfires, making it a promising tool for practical applications. In conclusion, the proposed wildfire detection system using Faster R-CNN has shown promising results in detecting wildfires in real-time. The system can be used to provide early warnings and help in the prevention and control of wildfires. With further development and refinement, this system can be a valuable tool in the fight against the devastating effects of wildfires.