scholarly journals Hybrid Deep Learning Models with Sparse Enhancement Technique for Detection of Newly Grown Tree Leaves

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2077 ◽  
Author(s):  
Shih-Yu Chen ◽  
Chinsu Lin ◽  
Guan-Jie Li ◽  
Yu-Chun Hsu ◽  
Keng-Hao Liu
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Life Cycle ◽  
Climate Changes ◽  
Imbalanced Data ◽  
Semantic Segmentation ◽  
Effect Of Temperature ◽  
Tree Leaves ◽  
The Difference ◽  
Segmentation Models

The life cycle of leaves, from sprout to senescence, is the phenomenon of regular changes such as budding, branching, leaf spreading, flowering, fruiting, leaf fall, and dormancy due to seasonal climate changes. It is the effect of temperature and moisture in the life cycle on physiological changes, so the detection of newly grown leaves (NGL) is helpful for the estimation of tree growth and even climate change. This study focused on the detection of NGL based on deep learning convolutional neural network (CNN) models with sparse enhancement (SE). As the NGL areas found in forest images have similar sparse characteristics, we used a sparse image to enhance the signal of the NGL. The difference between the NGL and the background could be further improved. We then proposed hybrid CNN models that combined U-net and SegNet features to perform image segmentation. As the NGL in the image were relatively small and tiny targets, in terms of data characteristics, they also belonged to the problem of imbalanced data. Therefore, this paper further proposed 3-Layer SegNet, 3-Layer U-SegNet, 2-Layer U-SegNet, and 2-Layer Conv-U-SegNet architectures to reduce the pooling degree of traditional semantic segmentation models, and used a loss function to increase the weight of the NGL. According to the experimental results, our proposed algorithms were indeed helpful for the image segmentation of NGL and could achieve better kappa results by 0.743.

scholarly journals Deep-Learning-Based Segmentation of Fresh or Young Concrete Sections from Images of Construction Sites

Materials ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6311
Author(s):  
Woldeamanuel Minwuye Mesfin ◽  
Soojin Cho ◽  
Jeongmin Lee ◽  
Hyeong-Ki Kim ◽  
Taehoon Kim
Keyword(s):  
Deep Learning ◽  
Training Image ◽  
Threshold Values ◽  
Construction Sites ◽  
Softmax Classifier ◽  
Incorrect Prediction ◽  
Young Concrete ◽  
Rgb Images ◽  
The Difference ◽  
Segmentation Models

The objective of this study is to evaluate the feasibility of deep-learning-based segmentation of the area covered by fresh and young concrete in the images of construction sites. The RGB images of construction sites under various actual situations were used as an input into several types of convolutional neural network (CNN)–based segmentation models, which were trained using training image sets. Various ranges of threshold values were applied for the classification, and their accuracy and recall capacity were quantified. The trained models could segment the concrete area overall although they were not able to judge the difference between concrete of different ages as professionals can. By increasing the threshold values for the softmax classifier, the cases of incorrect prediction as concrete became almost zero, while some areas of concrete became segmented as not concrete.

scholarly journals Drone Image Segmentation Using Machine and Deep Learning for Mapping Raised Bog Vegetation Communities

2020 ◽  
Vol 12 (16) ◽  
pp. 2602 ◽  
Author(s):  
Saheba Bhatnagar ◽  
Laurence Gill ◽  
Bidisha Ghosh
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Computation Time ◽  
Semantic Segmentation ◽  
Training Dataset ◽  
Atmospheric Conditions ◽  
Raised Bog ◽  
Vegetation Communities ◽  
Lower Accuracy ◽  
Cost Efficient

The application of drones has recently revolutionised the mapping of wetlands due to their high spatial resolution and the flexibility in capturing images. In this study, the drone imagery was used to map key vegetation communities in an Irish wetland, Clara Bog, for the spring season. The mapping, carried out through image segmentation or semantic segmentation, was performed using machine learning (ML) and deep learning (DL) algorithms. With the aim of identifying the most appropriate, cost-efficient, and accurate segmentation method, multiple ML classifiers and DL models were compared. Random forest (RF) was identified as the best pixel-based ML classifier, which provided good accuracy (≈85%) when used in conjunction graph cut algorithm for image segmentation. Amongst the DL networks, a convolutional neural network (CNN) architecture in a transfer learning framework was utilised. A combination of ResNet50 and SegNet architecture gave the best semantic segmentation results (≈90%). The high accuracy of DL networks was accompanied with significantly larger labelled training dataset, computation time and hardware requirements compared to ML classifiers with slightly lower accuracy. For specific applications such as wetland mapping where networks are required to be trained for each different site, topography, season, and other atmospheric conditions, ML classifiers proved to be a more pragmatic choice.

scholarly journals MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dominik Müller ◽  
Frank Kramer
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Medical Image ◽  
Cross Validation ◽  
Data Augmentation ◽  
Semantic Segmentation ◽  
Medical Image Segmentation ◽  
Tumor Segmentation ◽  
Data Set ◽  
Public Data

Abstract Background The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medical image segmentation pipelines. Already implemented pipelines are commonly standalone software, optimized on a specific public data set. Therefore, this paper introduces the open-source Python library MIScnn. Implementation The aim of MIScnn is to provide an intuitive API allowing fast building of medical image segmentation pipelines including data I/O, preprocessing, data augmentation, patch-wise analysis, metrics, a library with state-of-the-art deep learning models and model utilization like training, prediction, as well as fully automatic evaluation (e.g. cross-validation). Similarly, high configurability and multiple open interfaces allow full pipeline customization. Results Running a cross-validation with MIScnn on the Kidney Tumor Segmentation Challenge 2019 data set (multi-class semantic segmentation with 300 CT scans) resulted into a powerful predictor based on the standard 3D U-Net model. Conclusions With this experiment, we could show that the MIScnn framework enables researchers to rapidly set up a complete medical image segmentation pipeline by using just a few lines of code. The source code for MIScnn is available in the Git repository: https://github.com/frankkramer-lab/MIScnn.

scholarly journals Semantic Segmentation of Cabbage in the South Korea Highlands with Images by Unmanned Aerial Vehicles

2021 ◽  
Vol 11 (10) ◽  
pp. 4493
Author(s):  
Yongwon Jo ◽  
Soobin Lee ◽  
Youngjae Lee ◽  
Hyungu Kahng ◽  
Seonghun Park ◽  
...  
Keyword(s):  
Deep Learning ◽  
South Korea ◽  
Unmanned Aerial Vehicles ◽  
Rapid Development ◽  
Significant Proportion ◽  
Semantic Segmentation ◽  
Training Data ◽  
Field Surveys ◽  
Aerial Vehicles ◽  
Segmentation Models

Identifying agricultural fields that grow cabbage in the highlands of South Korea is critical for accurate crop yield estimation. Only grown for a limited time during the summer, highland cabbage accounts for a significant proportion of South Korea’s annual cabbage production. Thus, it has a profound effect on the formation of cabbage prices. Traditionally, labor-extensive and time-consuming field surveys are manually carried out to derive agricultural field maps of the highlands. Recently, high-resolution overhead images of the highlands have become readily available with the rapid development of unmanned aerial vehicles (UAV) and remote sensing technology. In addition, deep learning-based semantic segmentation models have quickly advanced by recent improvements in algorithms and computational resources. In this study, we propose a semantic segmentation framework based on state-of-the-art deep learning techniques to automate the process of identifying cabbage cultivation fields. We operated UAVs and collected 2010 multispectral images under different spatiotemporal conditions to measure how well semantic segmentation models generalize. Next, we manually labeled these images at a pixel-level to obtain ground truth labels for training. Our results demonstrate that our framework performs well in detecting cabbage fields not only in areas included in the training data but also in unseen areas not included in the training data. Moreover, we analyzed the effects of infrared wavelengths on the performance of identifying cabbage fields. Based on the results of our framework, we expect agricultural officials to reduce time and manpower when identifying information about highlands cabbage fields by replacing field surveys.

scholarly journals Semantic Segmentation of Satellite Images using Deep Learning

2021 ◽  
Vol 10 (8) ◽  
pp. 33-37
Author(s):  
Chandra Pal Kushwah ◽  
Kuruna Markam
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Learning Performance ◽  
Test Results ◽  
Natural Scene ◽  
Target Segmentation ◽  
Segmentation Models

Bidirectional in recent years, Deep learning performance in natural scene image processing has improved its use in remote sensing image analysis. In this paper, we used the semantic segmentation of remote sensing images for deep neural networks (DNN). To make it ideal for multi-target semantic segmentation of remote sensing image systems, we boost the Seg Net encoder-decoder CNN structures with index pooling & U-net. The findings reveal that the segmentation of various objects has its benefits and drawbacks for both models. Furthermore, we provide an integrated algorithm that incorporates two models. The test results indicate that the integrated algorithm proposed will take advantage of all multi-target segmentation models and obtain improved segmentation relative to two models.

scholarly journals Portrait Segmentation Using Ensemble of Heterogeneous Deep-Learning Models

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 197
Author(s):  
Yong-Woon Kim ◽  
Yung-Cheol Byun ◽  
Addapalli V. N. Krishna
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Medical Image Analysis ◽  
Learning Technology ◽  
Learning Models ◽  
Computing Power ◽  
Semantic Image Segmentation ◽  
Voting Method ◽  
Segmentation Models

Image segmentation plays a central role in a broad range of applications, such as medical image analysis, autonomous vehicles, video surveillance and augmented reality. Portrait segmentation, which is a subset of semantic image segmentation, is widely used as a preprocessing step in multiple applications such as security systems, entertainment applications, video conferences, etc. A substantial amount of deep learning-based portrait segmentation approaches have been developed, since the performance and accuracy of semantic image segmentation have improved significantly due to the recent introduction of deep learning technology. However, these approaches are limited to a single portrait segmentation model. In this paper, we propose a novel approach using an ensemble method by combining multiple heterogeneous deep-learning based portrait segmentation models to improve the segmentation performance. The Two-Models ensemble and Three-Models ensemble, using a simple soft voting method and weighted soft voting method, were experimented. Intersection over Union (IoU) metric, IoU standard deviation and false prediction rate were used to evaluate the performance. Cost efficiency was calculated to analyze the efficiency of segmentation. The experiment results show that the proposed ensemble approach can perform with higher accuracy and lower errors than single deep-learning-based portrait segmentation models. The results also show that the ensemble of deep-learning models typically increases the use of memory and computing power, although it also shows that the ensemble of deep-learning models can perform more efficiently than a single model with higher accuracy using less memory and less computing power.

scholarly journals A Deep Learning-Based Framework for Automated Extraction of Building Footprint Polygons from Very High-Resolution Aerial Imagery

2021 ◽  
Vol 13 (18) ◽  
pp. 3630
Author(s):  
Ziming Li ◽  
Qinchuan Xin ◽  
Ying Sun ◽  
Mengying Cao
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Semantic Segmentation ◽  
Aerial Imagery ◽  
Learning Models ◽  
Building Footprint ◽  
Bounding Boxes ◽  
Very High ◽  
Segmentation Models

Accurate building footprint polygons provide essential data for a wide range of urban applications. While deep learning models have been proposed to extract pixel-based building areas from remote sensing imagery, the direct vectorization of pixel-based building maps often leads to building footprint polygons with irregular shapes that are inconsistent with real building boundaries, making it difficult to use them in geospatial analysis. In this study, we proposed a novel deep learning-based framework for automated extraction of building footprint polygons (DLEBFP) from very high-resolution aerial imagery by combining deep learning models for different tasks. Our approach uses the U-Net, Cascade R-CNN, and Cascade CNN deep learning models to obtain building segmentation maps, building bounding boxes, and building corners, respectively, from very high-resolution remote sensing images. We used Delaunay triangulation to construct building footprint polygons based on the detected building corners with the constraints of building bounding boxes and building segmentation maps. Experiments on the Wuhan University building dataset and ISPRS Vaihingen dataset indicate that DLEBFP can perform well in extracting high-quality building footprint polygons. Compared with the other semantic segmentation models and the vector map generalization method, DLEBFP is able to achieve comparable mapping accuracies with semantic segmentation models on a pixel basis and generate building footprint polygons with concise edges and vertices with regular shapes that are close to the reference data. The promising performance indicates that our method has the potential to extract accurate building footprint polygons from remote sensing images for applications in geospatial analysis.

scholarly journals Portable framework to deploy deep learning segmentation models for medical images

2021 ◽  
Author(s):  
Aditi Iyer ◽  
Eve Locastro ◽  
Aditya Apte ◽  
Harini Veeraraghavan ◽  
Joseph O Deasy
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Open Source ◽  
Programming Languages ◽  
Operating Systems ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Post Processing ◽  
Clinical Workflow ◽  
Segmentation Models

Purpose: This work presents a framework for deployment of deep learning image segmentation models for medical images across different operating systems and programming languages. Methods: Computational Environment for Radiological Research (CERR) platform was extended for deploying deep learning-based segmentation models to leverage CERR's existing functionality for radiological data import, transformation, management, and visualization. The framework is compatible with MATLAB as well as GNU Octave and Python for license-free use. Pre and post processing configurations including parameters for pre-processing images, population of channels, and post-processing segmentations was standardized using JSON format. CPU and GPU implementations of pre-trained deep learning segmentation models were packaged using Singularity containers for use in Linux and Conda environment archives for Windows, macOS and Linux operating systems. The framework accepts images in various formats including DICOM and CERR's planC and outputs segmentation in various formats including DICOM RTSTRUCT and planC objects. The ability to access the results readily in planC format enables visualization as well as radiomics and dosimetric analysis. The framework can be readily deployed in clinical software such as MIM via their extensions. Results: The open-source, GPL copyrighted framework developed in this work has been successfully used to deploy Deep Learning based segmentation models for five in-house developed and published models. These models span various treatment sites (H&N, Lung and Prostate) and modalities (CT, MR). Documentation for their usage and demo workflow is provided at https://github.com/cerr/CERR/wiki/Auto-Segmentation-models. The framework has also been used in clinical workflow for segmenting images for treatment planning and for segmenting publicly available large datasets for outcomes studies. Conclusions: This work presented a comprehensive, open-source framework for deploying deep learning-based medical image segmentation models. The framework was used to translate the developed models to clinic as well as reproducible and consistent image segmentation across institutions, facilitating multi-institutional outcomes modeling studies.

scholarly journals Semantic Segmentation for Aerial Images: A Literature Review

2020 ◽  
Vol 2 (3) ◽  
pp. 133-139
Author(s):  
Yongki Christian Sanjaya ◽  
Alexander Agung Santoso Gunawan ◽  
Edy Irwansyah
Keyword(s):  
Image Segmentation ◽  
Review Paper ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Performance Measurements ◽  
Semantic Image Segmentation ◽  
Segmentation Methods ◽  
Big Picture ◽  
Segmentation Models

Semantic image segmentation is one of the fundamental applications of computer vision which can also be called pixel-level classification. Semantic image segmentation is the process of understanding the role of each pixel in an image. Over time, the model for completing Semantic Image Segmentation has developed very rapidly. Due to this rapid growth, many models related to Semantic Image Segmentation have been produced and have also been used or applied in many domains such as medical areas and intelligent transportation. Therefore, our motivation in making this paper is to contribute to the world of research by conducting a review of Semantic Image Segmentation which aims to provide a big picture related to the latest developments related to Semantic Image Segmentation. In addition, we also provide the results of performance measurements on each of the Semantic Image Segmentation methods that we discussed using the Intersectionover-Union (IoU) method. After that, we provide a comparison for each semantic image segmentation model that we discuss using the results of the IoU and then provide conclusions related to a model that has good performance. We hope this review paper can facilitate researchers in understanding the development of Semantic Image Segmentation in a shorter time, simplify understanding of the latest advancements in Semantic Image Segmentation, and can also be used as a reference for developing new Semantic Image Segmentation models in the future

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