Thermal images for wildfire core detection

The increasing number of wildfires damages nature and human life, making the early detection of wildfires in complex outdoor environments critical. With the advancement of drones and remote sensing technology, infrared cameras have become essential for wildfire detection. However, as the demand for higher accuracy in detection algorithms grows, the detection model's size and computational costs increase, making it challenging to deploy high-precision detection algorithms on edge computing devices onboard drones for real-time fire detection. This paper introduces a novel infrared wildfire detection network named FCDNet to tackle this issue. It includes an Efficient Processing (EP) module based on the novel Partial Depthwise Convolution (PDWConv) and the lightweight feature-sharing decoupled detection head (Fast Head), achieving low-size and low-computation wildfire detection. An Adaptive Sample Attention (ASA) Loss is introduced to enhance the detection accuracy of wildfire cores in combination with Normalized Wasserstein Distance (NWD) Loss. The experiment shows that the model size of FCDNet is only 4.0MB, representing 63.5% of the baseline YOLOv8n network, with 63.3% of its parameters. It operates at just 5 Giga Floating Point Operations Per Second (GFLOPs), 38.3% lower, and achieves a 77.5% mAP (@50-95 IOU), a 1% increase, with a 460×460 input image size. Compared to the state-of-the-art YOLOv11n, FCDNet reduces parameters, computation, and model size by 26.9%, 20.6%, and 27.3%, respectively. The thermal dataset and training codes used in this study are made publicly available at: https://github.com/WangLF1996/FCDNet-Dataset-and-Algorithm

野火频发的现象不仅破坏了自然环境，也对人类生活造成了严重影响，因此在复杂户外环境中对野火的早期探测显得尤为关键。随着无人机和遥感技术的不断发展，红外摄像头已成为野火探测不可或缺的工具。然而，随着对检测算法精度要求的不断提高，检测模型的大小和计算成本也随之增加，这使得在无人机上部署高精度检测算法以实现实时火情探测变得极具挑战性。本文提出了一种名为FCDNet的创新红外野火检测网络，以应对这一挑战。该网络包含一个基于新颖的局部深度卷积（PDWConv）的效率处理（EP）模块和轻量级特征共享解耦检测头（Fast Head），实现了小型化和低计算量的野火检测。此外，引入了一种自适应样本注意力（ASA）损失函数，与归一化Wasserstein距离（NWD）损失函数结合使用，以提升野火核心的检测精度。实验结果表明，FCDNet的模型大小仅为4.0MB，仅为基线YOLOv8n网络的63.5%，参数量占其63.3%。其运行速度为5 Giga Floating Point Operations Per Second（GFLOPs），比YOLOv11n降低了38.3%，在460×460的输入图像尺寸下，实现了77.5%的mAP（@50-95 IOU），比之前提高了1%。与最先进的YOLOv11n相比，FCDNet在参数量、计算量和模型大小方面分别降低了26.9%、20.6%和27.3%。本研究中所使用的热成像数据集和训练代码已公开发布于：https://github.com/WangLF1996/FCDNet-Dataset-and-Algorithm

搜集汇总

背景与挑战

背景概述

该数据集是一个用于野火核心检测的热成像数据集，旨在支持开发轻量、高效的红外野火检测算法。数据集主要用于训练和验证FCDNet网络，该网络通过创新的轻量化设计，在显著降低模型大小和计算成本的同时，提升了野火核心的检测精度，适用于无人机等边缘设备的实时部署。