收藏切换
Improved U2Net-Based Ground Cloud Image Segmentation Techniques
收藏切换
PDF
Kun-ning ZHAI1, Zhu-mu FU1, *, Xiu-ju WANG1, Hai-bing ZHU2, Jian-xing WEI2, Jun-rui CHANG2
Science Technology and Engineering | 2025, 25(14) : 5737 - 5744
Less
收藏切换
Science Technology and Engineering | 2025, 25(14): 5737-5744
Papers·Astronomy and Geosciences
Improved U2Net-Based Ground Cloud Image Segmentation Techniques
Full
Kun-ning ZHAI1, Zhu-mu FU1, *, Xiu-ju WANG1, Hai-bing ZHU2, Jian-xing WEI2, Jun-rui CHANG2
Affiliations
  • 1. School of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China
  • 2. Kaimai (Luoyang) Measurement and Control Co., Ltd., Luoyang 471000, China
Published: 2025-05-18 doi: 10.12404/j.issn.1671-1815.2308525
Outline
收藏切换

Clouds play a crucial role in the atmospheric dynamics of the Earth, and precise segmentation of ground-based cloud images is essential for improving the accuracy of weather forecasting. In response to issues such as varying data quality, low data volume, and different capture angles in existing open-source cloud image datasets, a labeled standard ground-based cloud image dataset (Cloud-GT) was constructed using manual annotation and transfer learning methods. The color channel component threshold segmentation method was employed to eliminate sunlight interference. Furthermore, an improved U2Net-based ground-based cloud image segmentation technique was proposed. The model introduced channel attention modules and depth-wise separable convolution modules in the feature extraction unit, which greatly reduces the network model parameters while improving the effective feature extraction of ground-based cloud maps within the network. Finally, comparing and analyzing the method with classical segmentation networks, experimental results indicated that the method achieved classification pixel accuracy, mean class pixel accuracy, average intersection over union, intersection over union, and F1 score of 84.03%, 90.88%, 84.13%, 74.12%, and 89.59%, respectively. In comparison with U2Net, UNet, and FCN, the method demonstrated a significant improvement in performance. In conclusion, the method not only substantially reduced the model parameters but also effectively enhanced segmentation accuracy, which provides the possibility of practical application.

foundation cloud map  /  image segmentation  /  U2Net  /  attention mechanism
Kun-ning ZHAI, Zhu-mu FU, Xiu-ju WANG, Hai-bing ZHU, Jian-xing WEI, Jun-rui CHANG. Improved U2Net-Based Ground Cloud Image Segmentation Techniques[J]. Science Technology and Engineering, 2025 , 25 (14) : 5737 -5744 . DOI: 10.12404/j.issn.1671-1815.2308525
Year 2025 volume 25 Issue 14
PDF
377
152
Cite this Article
BibTeX
Article Info
doi: 10.12404/j.issn.1671-1815.2308525
  • Receive Date:2023-11-01
  • Online Date:2025-07-09
  • Published:2025-05-18
Article Data
Affiliations
History
  • Received:2023-11-01
  • Revised:2025-02-12
Funding
Affiliations
    1. School of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China
    2. Kaimai (Luoyang) Measurement and Control Co., Ltd., Luoyang 471000, China
References
Share
https://castjournals.cast.org.cn/joweb/kxjsygc/EN/10.12404/j.issn.1671-1815.2308525
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT