论文标题
ACLNET：基于注意力和聚类的云分割网络
ACLNet: An Attention and Clustering-based Cloud Segmentation Network
论文作者
论文摘要
我们提出了一种名为ACLNET的新型深度学习模型，用于从地面图像中进行云分割。 ACLNET同时使用深神经网络和机器学习（ML）算法来提取互补功能。具体而言，它使用有效网络-B0作为主链，“`trous空间金字塔池池”（ASPP）在多个接收场上学习，而“全局注意模块”（GAM）从图像中提取细细节的细节。 ACLNET还使用K-均值聚类来更精确地提取云边界。 ACLNET对白天和夜间图像都有效。它提供的错误率较低，较高的召回率和更高的F1评分比阿特云分割模型。 ACLNET的源代码可在此处找到：https：//github.com/ckmvigil/aclnet。
We propose a novel deep learning model named ACLNet, for cloud segmentation from ground images. ACLNet uses both deep neural network and machine learning (ML) algorithm to extract complementary features. Specifically, it uses EfficientNet-B0 as the backbone, "`a trous spatial pyramid pooling" (ASPP) to learn at multiple receptive fields, and "global attention module" (GAM) to extract finegrained details from the image. ACLNet also uses k-means clustering to extract cloud boundaries more precisely. ACLNet is effective for both daytime and nighttime images. It provides lower error rate, higher recall and higher F1-score than state-of-art cloud segmentation models. The source-code of ACLNet is available here: https://github.com/ckmvigil/ACLNet.
