RFWNet: A Multiscale Remote Sensing Forest Wildfire Detection Network With Digital Twinning, Adaptive Spatial Aggregation, and Dynamic Sparse Features

Real-time detection of forest fires through remote sensing is a challenging task, especially in the context of limited data availability. In response to this challenge, this article leverages the digital twin (DT) concept to create a comprehensive and high-fidelity synthetic forest wildfire dataset. Alongside this, we have made available a high-resolution forest fire remote sensing dataset from real scenarios, meticulously collected and annotated by our research team. Aiming for precision in detecting forest fires via remote sensing, we present the remote sensing forest wildfire detection network (RFWNet) and its lightweight version, RFWNet-nano. More specifically, our network’s backbone, grounded on deformable convolution network v3 (DCNv3), develops a multigroup mechanism, amplifying its ability to perceive the correlations overextended distances. Utilizing our dual-path dynamic sparse attention (DDSA), we meld coarse-grained regional selection with granular token-to-token attention, adeptly capturing the evolving contours of fires and smoke. To address diverse scenarios, our Vanilla Head design, backed by a profound training approach and simultaneous stacked activations, accurately identifies flames and smoke across multiple scales. Furthermore, we advocate for a 24/7 real-time monitoring system, synergizing drones, edge computing devices, and NVIDIA GPUs. Our experimental outcomes indicate that relative to numerous prevailing object detection algorithms, RFWNet and RFWNet-nano both manifest considerable superiority in terms of quantitative precision and visual results, substantiating the robustness and preeminence of our methodologies.