4.6 Article

Deep Learning Approach for Accurate Segmentation of Sand Boils in Levee Systems

Related references

Note: Only part of the references are listed.
Article Computer Science, Information Systems

IterLUNet: Deep Learning Architecture for Pixel-Wise Crack Detection in Levee Systems

Manisha Panta et al.

Summary: This study presents a novel encoder-decoder-based fully convolutional neural network, Iterative Loop U-Net (IterLUNet), for automatic crack detection in levee images. By adding a loop-like structure in the U-Net-like architecture, IterLUNet effectively utilizes all the feature maps from encoders, bottlenecks, and decoders. It outperforms the state-of-the-art architectures on the levee system image dataset, achieving substantial improvement in Intersection over Union (IoU) compared to baseline U-Net model and other latest models such as MultiResUnet, Attention U-Net, and Unet++. Additionally, IterLUNet requires significantly fewer parameters for training, resulting in less space consumption for pixel-wise crack detection in AI-based inspection of levee systems.

IEEE ACCESS (2023)

Article Environmental Sciences

A Depth-Wise Separable U-Net Architecture with Multiscale Filters to Detect Sinkholes

Rasha Alshawi et al.

Summary: In this paper, an improved U-Net model is proposed, which achieves higher performance and reduced complexity by introducing a sparsely connected depth-wise separable block with multiscale filters. The use of depth-wise separable convolution significantly reduces trainable parameters, making the training faster and reducing the risk of overfitting. The proposed model outperforms state-of-the-art methods, achieving higher accuracy and IoU on the sinkhole and nuclei datasets.

REMOTE SENSING (2023)

Proceedings Paper Geosciences, Multidisciplinary

PIXEL-LEVEL CRACK DETECTION IN LEVEE SYSTEMS: A COMPARATIVE STUDY

Manisha Panta et al.

Summary: This study uses deep learning-based image segmentation methods to automatically detect levee cracks at the pixel level, aiming to improve the prevention and control of catastrophic flooding. The results show that the MultiResUnet model achieves the best performance on both training and testing data.

2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022) (2022)

Article Engineering, Electrical & Electronic

A Comprehensive Survey on Transfer Learning

Fuzhen Zhuang et al.

Summary: Transfer learning aims to improve the performance of target learners by transferring knowledge from related source domains, reducing the reliance on target-domain data. This survey aims to systematize and summarize existing research studies in order to help readers understand the current status and ideas in the area of transfer learning.

PROCEEDINGS OF THE IEEE (2021)

Article Engineering, Civil

Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

Di Feng et al.

Summary: Deep learning is driving recent advancements in perception for autonomous driving through the fusion of multiple sensors, but questions regarding network architecture design, fusion timing, and methods remain open. This review aims to systematically summarize methodologies for deep multi-modal object detection and semantic segmentation in autonomous driving, while also discussing challenges and open questions. The reviewed study provides an overview of the topic, fusion methodologies, and offers an interactive online platform for further exploration.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021)

Article Environmental Sciences

A machine learning approach to detecting cracks in levees and floodwalls

Aditi Kuchi et al.

Summary: Levees and floodwalls constructions are vulnerable to deterioration due to various factors, requiring continuous monitoring and maintenance. This research focuses on detecting cracks in these structures using digital images and implementing machine learning algorithms to improve accuracy.

REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT (2021)

Article Computer Science, Artificial Intelligence

MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation

Nabil Ibtehaz et al.

NEURAL NETWORKS (2020)

Article Biochemical Research Methods

Segmentation of roots in soil with U-Net

Abraham George Smith et al.

PLANT METHODS (2020)

Article Computer Science, Information Systems

Albumentations: Fast and Flexible Image Augmentations

Alexander Buslaev et al.

INFORMATION (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

Mohammad Hesam Hesamian et al.

JOURNAL OF DIGITAL IMAGING (2019)

Article Environmental Sciences

Evaluation of environmental predictors for sand boil formation: Rhine-Meuse Delta, Netherlands

Stephen N. Semmens et al.

ENVIRONMENTAL EARTH SCIENCES (2019)

Review Geography, Physical

Deep learning in remote sensing applications: A meta-analysis and review

Lei Ma et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2019)

Article Environmental Sciences

Deep Learning for Soil and Crop Segmentation from Remotely Sensed Data

Jack Dyson et al.

REMOTE SENSING (2019)

Article Computer Science, Artificial Intelligence

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

Liang-Chieh Chen et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2018)

Article Computer Science, Artificial Intelligence

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Vijay Badrinarayanan et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Xception: Deep Learning with Depthwise Separable Convolutions

Francois Chollet

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

Article Agriculture, Multidisciplinary

Optimal color space selection method for plant/soil segmentation in agriculture

J. L. Hernandez-Hernandez et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2016)

Article Statistics & Probability

Principal component analysis

Herve Abdi et al.

WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS (2010)

Article Engineering, Electrical & Electronic

Image analysis of soil micromorphology: Feature extraction, segmentation, and quality inference

P Maragos et al.

EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING (2004)

Article Engineering, Civil

Determination of critical head in soil piping

CSP Ojha et al.

JOURNAL OF HYDRAULIC ENGINEERING-ASCE (2003)