UCloudNet: A Residual U-Net with Deep Supervision for Cloud Image Segmentation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909454420672512 |
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| author | Li, Yijie Wang, Hewei Wang, Shaofan Lee, Yee Hui Pathan, Muhammad Salman Dev, Soumyabrata |
| author_facet | Li, Yijie Wang, Hewei Wang, Shaofan Lee, Yee Hui Pathan, Muhammad Salman Dev, Soumyabrata |
| contents | Recent advancements in meteorology involve the use of ground-based sky cameras for cloud observation. Analyzing images from these cameras helps in calculating cloud coverage and understanding atmospheric phenomena. Traditionally, cloud image segmentation relied on conventional computer vision techniques. However, with the advent of deep learning, convolutional neural networks (CNNs) are increasingly applied for this purpose. Despite their effectiveness, CNNs often require many epochs to converge, posing challenges for real-time processing in sky camera systems. In this paper, we introduce a residual U-Net with deep supervision for cloud segmentation which provides better accuracy than previous approaches, and with less training consumption. By utilizing residual connection in encoders of UCloudNet, the feature extraction ability is further improved. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06440 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UCloudNet: A Residual U-Net with Deep Supervision for Cloud Image Segmentation Li, Yijie Wang, Hewei Wang, Shaofan Lee, Yee Hui Pathan, Muhammad Salman Dev, Soumyabrata Computer Vision and Pattern Recognition Image and Video Processing Recent advancements in meteorology involve the use of ground-based sky cameras for cloud observation. Analyzing images from these cameras helps in calculating cloud coverage and understanding atmospheric phenomena. Traditionally, cloud image segmentation relied on conventional computer vision techniques. However, with the advent of deep learning, convolutional neural networks (CNNs) are increasingly applied for this purpose. Despite their effectiveness, CNNs often require many epochs to converge, posing challenges for real-time processing in sky camera systems. In this paper, we introduce a residual U-Net with deep supervision for cloud segmentation which provides better accuracy than previous approaches, and with less training consumption. By utilizing residual connection in encoders of UCloudNet, the feature extraction ability is further improved. |
| title | UCloudNet: A Residual U-Net with Deep Supervision for Cloud Image Segmentation |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2501.06440 |