Ground-based cloud recognition method based on an improved DeepLabV3+ model

IF 1.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Cognitive Computation and Systems Pub Date : 2023-10-27 DOI:10.1049/ccs2.12091
Yue Liang, Quanbo Ge
{"title":"Ground-based cloud recognition method based on an improved DeepLabV3+ model","authors":"Yue Liang,&nbsp;Quanbo Ge","doi":"10.1049/ccs2.12091","DOIUrl":null,"url":null,"abstract":"<p>An improved method for recognising clouds on the ground map is proposed incorporating the DeepLabV3+ model to solve three issues. Firstly, an image preprocessing module is developed to enrich image quality, particularly at night, as clouds can appear to be too blurry to distinguish. Secondly, the CBAM attention mechanism is applied to safeguard texture and boundary information, ensuring the depiction of cirrus edges is not lost. Finally, the feature extraction network can be optimised to enhance the model and decrease its computational complexity. In comparison to the original model, the accuracy of the proposed method increased by 10.89%, resulting in an overall accuracy of 94.18%. Furthermore, the MIoU has improved from 66.02% to 79.31%. The number of parameters was reduced by 51.45% to 13.4 M. Of the various cloud types, the improvement in cirrus is particularly striking, with the MIoU increasing from 1.78% to 56.01%.</p>","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"5 4","pages":"280-287"},"PeriodicalIF":1.2000,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ccs2.12091","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognitive Computation and Systems","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/ccs2.12091","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

An improved method for recognising clouds on the ground map is proposed incorporating the DeepLabV3+ model to solve three issues. Firstly, an image preprocessing module is developed to enrich image quality, particularly at night, as clouds can appear to be too blurry to distinguish. Secondly, the CBAM attention mechanism is applied to safeguard texture and boundary information, ensuring the depiction of cirrus edges is not lost. Finally, the feature extraction network can be optimised to enhance the model and decrease its computational complexity. In comparison to the original model, the accuracy of the proposed method increased by 10.89%, resulting in an overall accuracy of 94.18%. Furthermore, the MIoU has improved from 66.02% to 79.31%. The number of parameters was reduced by 51.45% to 13.4 M. Of the various cloud types, the improvement in cirrus is particularly striking, with the MIoU increasing from 1.78% to 56.01%.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于改进的 DeepLabV3+ 模型的地基云识别方法
结合DeepLabV3+模型,提出了一种改进的地面地图云识别方法,解决了三个问题。首先,开发了图像预处理模块来丰富图像质量,特别是在夜间,因为云可能看起来太模糊而无法区分。其次,利用CBAM注意机制保护纹理和边界信息,保证卷云边缘的描述不丢失;最后,对特征提取网络进行优化,增强模型,降低计算复杂度。与原模型相比,本文方法的准确率提高了10.89%,总体准确率达到94.18%。MIoU从66.02%提高到79.31%。参数个数减少了51.45%,为13.4 m。在各种云种中,卷云的改善尤为显著,MIoU从1.78%增加到56.01%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Cognitive Computation and Systems
Cognitive Computation and Systems Computer Science-Computer Science Applications
CiteScore
2.50
自引率
0.00%
发文量
39
审稿时长
10 weeks
期刊最新文献
Emotion-aware psychological first aid: Integrating BERT-based emotional distress detection with Psychological First Aid-Generative Pre-Trained Transformer chatbot for mental health support Brain network analysis of benign childhood epilepsy with centrotemporal spikes: With versus without interictal spikes Garbage prediction using regression analysis for municipal corporations of Indian cities MedBlockSure: Blockchain-based insurance system Advancing low-light object detection with you only look once models: An empirical study and performance evaluation
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1