Traditional forest-fire recognition based on the characteristics of smoke, temperature and light fails to accurately detect and respond to early fires. By analyzing the characteristics of flame, the methods based on a...Traditional forest-fire recognition based on the characteristics of smoke, temperature and light fails to accurately detect and respond to early fires. By analyzing the characteristics of flame, the methods based on aerial image recognition have been widely used, such as RGB-based and HIS-based methods. However, these methods are susceptible to background factors causing interference and false detection. To alleviate these problems, we investigate two subspace clustering methods based on sparse and collaborative representation, respectively, to detect and locate forest fires. Firstly, subspace clustering segments flame from the whole image. Afterwards, sparse or collaborative representation is employed to represent most of the flame information in a dictionary with l1-regularization or l2-regularization term, which results in fewer reconstruction errors. Experimental results show that the proposed SSC and CSC substantially outperform the state-of-the art methods.展开更多
The influences of finite-size effects on the self-organized criticality (SOC) of the traditional forest-fire model are investigated by means of a new method. The forest size is originally set to a value much greater t...The influences of finite-size effects on the self-organized criticality (SOC) of the traditional forest-fire model are investigated by means of a new method. The forest size is originally set to a value much greater than the correla-tion length of the forest. Finite-size effects are then studied by equally dividing the forest into more and more separate subsystems on condition that the forest size, igniting prob-ability and planting probability are invariant. A new phe-nomenon, i.e. the finite-size effects with one-side frequency peak, is observed. The boundary between two neighboring subsystems can be regarded as a firebreak. The concept of ’separation ability’ is introduced to represent the probability for the firebreak to block off the fire successfully. Restrain-ing effects of separation ability on finite-size effects are ana-lyzed. Finite-size effects and separation ability, as well as their relations are found to have practical importance to the actual forest-fire protection.展开更多
森林火点检测在林火应急救援中起着至关重要的作用.鉴于现有模型在样本质量、多尺度检测以及多视角图像泛化能力方面存在不足,以YOLOv7为基础,提出一种森林火点目标检测方法FFD-YOLO(forest fire detection based on YOLO).首先,构建多...森林火点检测在林火应急救援中起着至关重要的作用.鉴于现有模型在样本质量、多尺度检测以及多视角图像泛化能力方面存在不足,以YOLOv7为基础,提出一种森林火点目标检测方法FFD-YOLO(forest fire detection based on YOLO).首先,构建多视角可见光图像森林火灾高点检测数据集FFHPV(forest fire of high point view),旨在增强模型对多视角火点知识的学习能力;其次,引入全维动态卷积,构建空间金字塔池化层(OD-SPP),以此提升模型针对多视角数据的火点特征提取能力;最后,引入具有动态非单调聚焦机制的边界框定位损失函数Wise-IoU(wise intersection over union),降低低质量数据对模型精度的影响,提高小目标火点的检测能力.实验结果表明:所提出的FFD-YOLO方法相较于YOLOv7,精度提高3.9%,召回率提高3.7%,均值平均精度提高4.0%,F1分数提高0.038;同时,在与YOLOv5、YOLOv8、DDQ(dense distinct query)、DINO(detection transformer with improved denoising anchor boxes)、Faster R-CNN、Sparse R-CNN、Mask R-CNN、FCOS和YOLOX的对比实验中,FFD-YOLO具有最高的精度75.3%、召回率73.8%、均值平均精度77.6%和F1分数0.745,验证了该方法的可行性与有效性.展开更多
文摘Traditional forest-fire recognition based on the characteristics of smoke, temperature and light fails to accurately detect and respond to early fires. By analyzing the characteristics of flame, the methods based on aerial image recognition have been widely used, such as RGB-based and HIS-based methods. However, these methods are susceptible to background factors causing interference and false detection. To alleviate these problems, we investigate two subspace clustering methods based on sparse and collaborative representation, respectively, to detect and locate forest fires. Firstly, subspace clustering segments flame from the whole image. Afterwards, sparse or collaborative representation is employed to represent most of the flame information in a dictionary with l1-regularization or l2-regularization term, which results in fewer reconstruction errors. Experimental results show that the proposed SSC and CSC substantially outperform the state-of-the art methods.
基金This work was supported by the National Basic Research "973" Project in China the National Natural Science Foundation in China (Grant Nos. 59876039, 59936140 and 39970621).
文摘The influences of finite-size effects on the self-organized criticality (SOC) of the traditional forest-fire model are investigated by means of a new method. The forest size is originally set to a value much greater than the correla-tion length of the forest. Finite-size effects are then studied by equally dividing the forest into more and more separate subsystems on condition that the forest size, igniting prob-ability and planting probability are invariant. A new phe-nomenon, i.e. the finite-size effects with one-side frequency peak, is observed. The boundary between two neighboring subsystems can be regarded as a firebreak. The concept of ’separation ability’ is introduced to represent the probability for the firebreak to block off the fire successfully. Restrain-ing effects of separation ability on finite-size effects are ana-lyzed. Finite-size effects and separation ability, as well as their relations are found to have practical importance to the actual forest-fire protection.
文摘森林火点检测在林火应急救援中起着至关重要的作用.鉴于现有模型在样本质量、多尺度检测以及多视角图像泛化能力方面存在不足,以YOLOv7为基础,提出一种森林火点目标检测方法FFD-YOLO(forest fire detection based on YOLO).首先,构建多视角可见光图像森林火灾高点检测数据集FFHPV(forest fire of high point view),旨在增强模型对多视角火点知识的学习能力;其次,引入全维动态卷积,构建空间金字塔池化层(OD-SPP),以此提升模型针对多视角数据的火点特征提取能力;最后,引入具有动态非单调聚焦机制的边界框定位损失函数Wise-IoU(wise intersection over union),降低低质量数据对模型精度的影响,提高小目标火点的检测能力.实验结果表明:所提出的FFD-YOLO方法相较于YOLOv7,精度提高3.9%,召回率提高3.7%,均值平均精度提高4.0%,F1分数提高0.038;同时,在与YOLOv5、YOLOv8、DDQ(dense distinct query)、DINO(detection transformer with improved denoising anchor boxes)、Faster R-CNN、Sparse R-CNN、Mask R-CNN、FCOS和YOLOX的对比实验中,FFD-YOLO具有最高的精度75.3%、召回率73.8%、均值平均精度77.6%和F1分数0.745,验证了该方法的可行性与有效性.
