CloudU-Net: A Deep Convolutional Neural Network Architecture for Daytime and Nighttime Cloud Images’ Segmentation

Shi, Chaojun; Zhou, Yatong; Qiu, Bo; Guo, Dongjiao; Li, Mengci

doi:10.1109/lgrs.2020.3009227

Cited by 31 publications

(17 citation statements)

References 22 publications

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“…The max-Pooling function picks the maximum value from each kernel, the highest value creates a significant impact in the image [44]. When the kernel size is 2×2, half of the values denote the actual value, which increases the receptive field.…”

Section: B Max Layer (Max Pooling 2×2)mentioning

confidence: 99%

FHI-Unet: Faster Heterogeneous Images Semantic Segmentation Design and Edge AI Implementation for Visible and Thermal Images Processing

et al. 2022

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The same class of objects clustering process in a frame is known as semantic segmentation. The deep convolutional neural network-based semantic segmentation needs large-scale computations and annotations for data training to reach real-time inference speeds. The heterogeneous image segmentation is a more challenging task to categorize each pixel of an image. However, the heterogeneous image semantic segmentation method extracts the features of visible and thermal images separately. We designed an efficient architecture with the multi-hybrid-autoencoder and decoder for Faster Heterogeneous Image (FHI) Semantic Segmentation. The proposed corresponding architecture has fewer layers resulting in lower parameters, higher inference speed, and Intersection over Union (IoU). The specialty of this architecture is the discrete autonomous feature extraction framework for RGB image and Thermal (T) image inputs with individual convolutional layers. Later, we combined the 4-channels (RGBT) convolution features to reduce computational complexity and robust the model performances. The proposed FHI-Unet semantic segmentation model experimented on NVIDIA Xavier NX edge AI platforms with standard accuracy under the real-time inference requirement. The proposed FHI-Unet model has the highest mIoU of 43.67 and the fastest real-time inference of 83.39 frames per second on edge AI implementation. The proposed approach improves 31.36% inference speed, 7.16% mAcc, and 5.1% mIoU on the Multi-spectral Semantic Segmentation Dataset compared with the existing works.

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Section: B Max Layer (Max Pooling 2×2)mentioning

confidence: 99%

FHI-Unet: Faster Heterogeneous Images Semantic Segmentation Design and Edge AI Implementation for Visible and Thermal Images Processing

et al. 2022

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“…Likewise, a Long Short Term Memory for Neural network framework has been used to examine price tendency. On the other hand, a method in neural network architecture named CloudU-Net forGround-based Daytime and Nighttime Cloud Images (GDNCI) used has been presented in [315], that is used in the weather forecast; as observed in the paper, its performances have been improved for daytime and nighttime cloud images. As opposed to [316] that An innovative Multilevel Attention-based U-shape Graph Neural Network (MAUGNN) has been introduced, and the authors' purpose was using (MAUGNN) is to learn point cloud effectively due to 3-D sensors in the IoT industry.…”

Section: Ai In Cloudmentioning

confidence: 99%

Privacy-Preserving Cloud Computing: Ecosystem, Life Cycle, Layered Architecture and Future Roadmap

Ahmadi¹,

Salehfar²

2022

Preprint

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Privacy-Preserving Cloud Computing is an emerging technology with many applications in various fields. Cloud computing is important because it allows for scalability, adaptability, and improved security. Likewise, privacy in cloud computing is important because it ensures that the integrity of data stored on the cloud maintains intact. This survey paper on privacy-preserving cloud computing can help pave the way for future research in related areas. This paper helps to identify existing trends by establishing a layered architecture along with a life cycle and an ecosystem for privacy-preserving cloud systems in addition to identifying the existing trends in research on this area.

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“…Previous works have performed cloud segmentation on images taken from satellites (Xie et al 2017), and those taken from the ground (Dev, Lee, and Winkler 2016;Shi et al 2020;Xie et al 2020). We review those works that analyze cloud images taken from ground.…”

Section: Introductionmentioning

confidence: 99%

“…They modify the VGG16 network by replacing the fully connected layers with the decoder network. "CloudU-Net" (Shi et al 2020) is inspired from U-Net and it uses "dilated convolutions" and "fully connected conditional random field (CRF)". It also uses a lookahead optimizer for faster model convergence.…”

Section: Introductionmentioning

confidence: 99%

ACLNet: an attention and clustering-based cloud segmentation network

Makwana¹,

Nag

Susladkar³

et al. 2022

Remote Sensing Letters

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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, "à 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.

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CloudU-Net: A Deep Convolutional Neural Network Architecture for Daytime and Nighttime Cloud Images’ Segmentation

Cited by 31 publications

References 22 publications

FHI-Unet: Faster Heterogeneous Images Semantic Segmentation Design and Edge AI Implementation for Visible and Thermal Images Processing

FHI-Unet: Faster Heterogeneous Images Semantic Segmentation Design and Edge AI Implementation for Visible and Thermal Images Processing

Privacy-Preserving Cloud Computing: Ecosystem, Life Cycle, Layered Architecture and Future Roadmap

ACLNet: an attention and clustering-based cloud segmentation network

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