基于改进MobileNetV2的地基云图分割网络

Ground-Based Cloud Image Segmentation Network Based on Improved MobileNetV2

在大气测量领域,云是大气模型中最不确定的因素,对云图进行准确分割识别必不可少。然而由于云和大气条件的随机性,云图分割精度和准确率等方面存在着挑战。针对这一问题,设计了一种名为CloudHS-Net的新型网络,该网络基于MobileNetV2,通过引入混合拼接结构、空洞卷积和混合空洞的设计思想,结合高效通道注意力机制,用于实际云图的分割测量。在SWIMSEG和HHCL-Cloud数据集上进行了与其他先进模型的对比测试,以深入了解网络性能和各部分结构的作用。实验结果表明,高效通道注意力和混合拼接结构均能有效提升模型的分割性能。与当前先进的地基云图分割网络相比,CloudHS-Net在天空云图分割任务中表现出色,准确率达到95.51%,平均交并比达到89.86%。该模型成功降低了来自大气环境的干扰,如太阳光线等,加强了对云的关注,提高了云图分割的精度,更为精准地获取了云的覆盖状态,实验结果表明了该方法的可行性。

In the field of atmospheric measurement,clouds are the most uncertain factor in atmospheric models,so accurate segmentation and recognition of cloud image are indispensable.However,due to the stochastic nature of clouds and atmospheric conditions,challenges exist in the precision and accuracy of cloud image segmentation.To address this issue,we propose a novel network named CloudHS-Net based on MobileNetV2.This network incorporates a hybrid concatenation structure,dilated convolutions,and a mixed dilated design,along with an efficient channel attention mechanism,for practical cloud image segmentation.The performance of the network is thoroughly evaluated on the SWIMSEG and HHCL-Cloud datasets through comparative tests with other advanced models,providing insights into the network’s performance and the roles of its various components.Experimental results demonstrate that the efficient channel attention and hybrid concatenation structures effectively enhance the segmentation performance of the model.Compared to current advanced ground-based cloud image segmentation networks,CloudHS-Net excels in the task of sky cloud image segmentation,achieving an accuracy of 95.51%and mean intersection over union(MIoU)of 89.86%.The model reduces disturbances originating from atmospheric environment,such as sunlight,pay stronger attention to cloud.This leads to enhanced precision in cloud image segmentation,allowing for a more accurate capture of cloud coverage status and the experimental results show that the method is feasible.