Autonomous Drone Swarms for Environmental Monitoring and Wildfire Detection Using Edge Intelligence

Abstract

Wildfires are one of the serious environmental and ecological hazards resulting in massive loss of forest ecosystems, wild animals, and human settlements. Timely identification and active surveillance of wildfires will play a crucial role in reducing the area of the wildfire and responding promptly to the disaster. This paper offers an independent drone swarm system to monitor nature and detect wildfires through the use of unmanned aerial vehicles (UAVs), edge intelligence, and deep learning technologies. The system makes use of swarming UAVs, fitted with RGB and radiometric thermal sensors, to survey high-resolution aerial images of wildfire-prone areas. A deep learning model in multimodal is created to process the visual and thermal data and provide a precise indication of the wildfire along with fire region division. The FLAME 3 Dataset is used to conduct the experimental assessment that offers synchronized UAV-based RGB and thermal wildfire imagery as well as fire segmentation annotations. In order to facilitate real-time analysis and minimize communication latency, the trained deep learning model is deployed in edge computing modules that are packaged in UAV platforms. Edge intelligence enables every drone to interpret acquired imagery on the ground, identify the area of the wildfire, and create instantaneous alert messages without any full dependency on centralized cloud services. Another thing is that by use of a swarm coordination mechanism, a group of UAVs is able to monitor large areas of forest, exchange environmental information, and maximize coverage. The framework is tested based on the standard machine learning metrics such as accuracy, precision, recall, F1-score, and Intersection over Union (IoU), as well as operational metrics such as the detection latency and monitoring coverage efficiency. The experimental results demonstrate that the proposed system achieves an accuracy of 94.3%, precision of 92.8%, recall of 93.6%, F1-score of 93.2%, and IoU score of 89.5%, while maintaining a low detection latency of 1.8 seconds and achieving a monitoring coverage efficiency of 92.4%. The experimental results demonstrate that multimodal UAV imagery, edge-based processing, and swarm coordination methods allow improvement of accuracy in wildfire detection and maximize efficiency in wildfire monitoring significantly. The proposed solution is a very efficient and scaled way of protecting the environment in real time and early warning of wildfires in large forest habitats.