SegNet: A Segmented Deep Learning based Convolutional Neural Network Approach for Drones Wildfire Detection

arXiv:2405.00031v1 Announce Type: new
Abstract: This research addresses the pressing challenge of enhancing processing times and detection capabilities in Unmanned Aerial Vehicle (UAV)/drone imagery for global wildfire detection, despite limited datasets. Proposing a Segmented Neural Network (SegNet) selection approach, we focus on reducing feature maps to boost both time resolution and accuracy significantly advancing processing speeds and accuracy in real-time wildfire detection. This paper contributes to increased processing speeds enabling real-time detection capabilities for wildfire, increased detection accuracy of wildfire, …

abstract aerial arxiv capabilities challenge convolutional convolutional neural network cs.ai cs.cv datasets deep learning detection drone drones eess.iv feature focus global maps network neural network processing research type unmanned aerial vehicle wildfire