基于概率两阶段CenterNet2的林火图像预警检测方法

A forest fire image early warning detection method based on probabilistic two-stage CenterNet2

森林火灾的及时预警对于森林保护有着至关重要的作用,由于林中烟火背景复杂、干扰因素较多,检测精度和效率均会受到影响。为此提出了一种基于CenterNet2的林火图像检测方法,采用轻量级主干网络VoVNetV2结合非对称卷积核来提高特征提取能力和检测速度,同时在加权双向特征金字塔网络中引入注意力机制eSE进行特征融合,提高小目标检测精度,最后采用SIoU损失函数提升目标框回归效果。仿真实验结果表明,该方法能够较准确地进行实时林火检测,且漏报率低。

Timely warning of forest fire plays a crucial role in forest protection.Due to the complex background of forest fireworks and many interference factors,the detection accuracy and efficiency are affected.Therefore,a forest fire image detection method based on CenterNet2 is proposed.The lightweight backbone network VoVNetV2 combined with asymmetric convolution kernel is used to improve the feature extraction ability and detection speed.Meanwhile,an attention mechanism eSE(Effective Squeeze and Extraction)is introduced into the weighted bidirectional feature pyramid network for feature fusion,so as to improve the accuracy of small target detection.Then,SIoU loss function is used to improve the effect of target box regression.The simulation results show that the method can accurately detect forest fire in real time,and the false rate is low.