Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

Deep Learning (DL) is the state-of-the-art machine learning technology, which shows superior performance in computer vision, bioinformatics, natural language processing, and other areas. Especially as a modern image processing technology, DL has been successfully applied in various tasks, such as object detection, semantic segmentation, and scene analysis. However, with the increase of dense scenes in reality, due to severe occlusions, and small size of objects, the analysis of dense scenes becomes particularly challenging. To overcome these problems, DL recently has been increasingly applied to dense scenes and has begun to be used in dense agricultural scenes. The purpose of this review is to explore the applications of DL for dense scenes analysis in agriculture. In order to better elaborate the topic, we first describe the types of dense scenes in agriculture, as well as the challenges. Next, we introduce various popular deep neural networks used in these dense scenes. Then, the applications of these structures in various agricultural tasks are comprehensively introduced in this review, including recognition and classification, detection, counting and yield estimation. Finally, the surveyed DL applications, limitations and the future work for analysis of dense images in agriculture are summarized.

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Transformer-Based Decoder Designs for Semantic Segmentation on Remotely Sensed Images

Remote Sensing ◽

10.3390/rs13245100 ◽

2021 ◽

Vol 13 (24) ◽

pp. 5100

Author(s):

Teerapong Panboonyuen ◽

Kulsawasd Jitkajornwanich ◽

Siam Lawawirojwong ◽

Panu Srestasathiern ◽

Peerapon Vateekul

Keyword(s):

Image Processing ◽

Deep Learning ◽

Natural Language Processing ◽

Language Processing ◽

State Of The Art ◽

Semantic Segmentation ◽

Landsat 8 ◽

Convolutional Network ◽

Image Labeling ◽

Feature Pyramid

Transformers have demonstrated remarkable accomplishments in several natural language processing (NLP) tasks as well as image processing tasks. Herein, we present a deep-learning (DL) model that is capable of improving the semantic segmentation network in two ways. First, utilizing the pre-training Swin Transformer (SwinTF) under Vision Transformer (ViT) as a backbone, the model weights downstream tasks by joining task layers upon the pretrained encoder. Secondly, decoder designs are applied to our DL network with three decoder designs, U-Net, pyramid scene parsing (PSP) network, and feature pyramid network (FPN), to perform pixel-level segmentation. The results are compared with other image labeling state of the art (SOTA) methods, such as global convolutional network (GCN) and ViT. Extensive experiments show that our Swin Transformer (SwinTF) with decoder designs reached a new state of the art on the Thailand Isan Landsat-8 corpus (89.8% F1 score), Thailand North Landsat-8 corpus (63.12% F1 score), and competitive results on ISPRS Vaihingen. Moreover, both our best-proposed methods (SwinTF-PSP and SwinTF-FPN) even outperformed SwinTF with supervised pre-training ViT on the ImageNet-1K in the Thailand, Landsat-8, and ISPRS Vaihingen corpora.

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Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal

Sensors ◽

10.3390/s20061579 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1579

Author(s):

Dongqi Wang ◽

Qinghua Meng ◽

Dongming Chen ◽

Hupo Zhang ◽

Lisheng Xu

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Deep Neural Networks ◽

Channel Model ◽

Expert Knowledge ◽

Automatic Detection ◽

Data Representation ◽

Learning Technology ◽

Arrhythmia Detection ◽

Automatic Feature Extraction

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

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Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning

Energies ◽

10.3390/en14133800 ◽

2021 ◽

Vol 14 (13) ◽

pp. 3800

Author(s):

Sebastian Krapf ◽

Nils Kemmerzell ◽

Syed Khawaja Haseeb Khawaja Haseeb Uddin ◽

Manuel Hack Hack Vázquez ◽

Fabian Netzler ◽

...

Keyword(s):

Deep Learning ◽

System Analysis ◽

State Of The Art ◽

Critical Role ◽

Semantic Segmentation ◽

Energy System ◽

Aerial Images ◽

Potential Analysis ◽

3D Data ◽

Challenges And Opportunities

Roof-mounted photovoltaic systems play a critical role in the global transition to renewable energy generation. An analysis of roof photovoltaic potential is an important tool for supporting decision-making and for accelerating new installations. State of the art uses 3D data to conduct potential analyses with high spatial resolution, limiting the study area to places with available 3D data. Recent advances in deep learning allow the required roof information from aerial images to be extracted. Furthermore, most publications consider the technical photovoltaic potential, and only a few publications determine the photovoltaic economic potential. Therefore, this paper extends state of the art by proposing and applying a methodology for scalable economic photovoltaic potential analysis using aerial images and deep learning. Two convolutional neural networks are trained for semantic segmentation of roof segments and superstructures and achieve an Intersection over Union values of 0.84 and 0.64, respectively. We calculated the internal rate of return of each roof segment for 71 buildings in a small study area. A comparison of this paper’s methodology with a 3D-based analysis discusses its benefits and disadvantages. The proposed methodology uses only publicly available data and is potentially scalable to the global level. However, this poses a variety of research challenges and opportunities, which are summarized with a focus on the application of deep learning, economic photovoltaic potential analysis, and energy system analysis.

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InstantDL: an easy-to-use deep learning pipeline for image segmentation and classification

BMC Bioinformatics ◽

10.1186/s12859-021-04037-3 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Dominik Jens Elias Waibel ◽

Sayedali Shetab Boushehri ◽

Carsten Marr

Keyword(s):

Image Processing ◽

Deep Learning ◽

Specific Problem ◽

State Of The Art ◽

Image Data ◽

Semantic Segmentation ◽

Parameter Tuning ◽

Cellular Processes ◽

Minimal Effort ◽

Instance Segmentation

Abstract Background Deep learning contributes to uncovering molecular and cellular processes with highly performant algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate and fast image data processing. However, published algorithms mostly solve only one specific problem and they typically require a considerable coding effort and machine learning background for their application. Results We have thus developed InstantDL, a deep learning pipeline for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables researchers with a basic computational background to apply debugged and benchmarked state-of-the-art deep learning algorithms to their own data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows assessing the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible and well documented. Conclusions With InstantDL, we hope to empower biomedical researchers to conduct reproducible image processing with a convenient and easy-to-use pipeline.

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Representing Deep Neural Networks Latent Space Geometries with Graphs

Algorithms ◽

10.3390/a14020039 ◽

2021 ◽

Vol 14 (2) ◽

pp. 39

Author(s):

Carlos Lassance ◽

Vincent Gripon ◽

Antonio Ortega

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Objective Function ◽

Learning Process ◽

Deep Neural Networks ◽

State Of The Art ◽

The Core ◽

Learning Tasks ◽

Latent Space

Deep Learning (DL) has attracted a lot of attention for its ability to reach state-of-the-art performance in many machine learning tasks. The core principle of DL methods consists of training composite architectures in an end-to-end fashion, where inputs are associated with outputs trained to optimize an objective function. Because of their compositional nature, DL architectures naturally exhibit several intermediate representations of the inputs, which belong to so-called latent spaces. When treated individually, these intermediate representations are most of the time unconstrained during the learning process, as it is unclear which properties should be favored. However, when processing a batch of inputs concurrently, the corresponding set of intermediate representations exhibit relations (what we call a geometry) on which desired properties can be sought. In this work, we show that it is possible to introduce constraints on these latent geometries to address various problems. In more detail, we propose to represent geometries by constructing similarity graphs from the intermediate representations obtained when processing a batch of inputs. By constraining these Latent Geometry Graphs (LGGs), we address the three following problems: (i) reproducing the behavior of a teacher architecture is achieved by mimicking its geometry, (ii) designing efficient embeddings for classification is achieved by targeting specific geometries, and (iii) robustness to deviations on inputs is achieved via enforcing smooth variation of geometry between consecutive latent spaces. Using standard vision benchmarks, we demonstrate the ability of the proposed geometry-based methods in solving the considered problems.

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Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽

10.3390/s20102778 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2778 ◽

Cited By ~ 12

Author(s):

Mohsen Azimi ◽

Armin Eslamlou ◽

Gokhan Pekcan

Keyword(s):

Deep Learning ◽

Structural Health Monitoring ◽

Health Monitoring ◽

High Speed ◽

Deep Neural Networks ◽

State Of The Art ◽

Data Driven ◽

Structural Health ◽

Promising Tool ◽

Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

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A Survey of Graphical Page Object Detection with Deep Neural Networks

10.20944/preprints202104.0739.v1 ◽

2021 ◽

Author(s):

Jwalin Bhatt ◽

Khurram Azeem Hashmi ◽

Muhammad Zeshan Afzal ◽

Didier Stricker

Keyword(s):

Deep Learning ◽

Object Detection ◽

Conceptual Understanding ◽

Deep Neural Networks ◽

State Of The Art ◽

Learning Approaches ◽

Document Images ◽

Essential Information ◽

Current State ◽

High Level

In any document, graphical elements like tables, figures, and formulas contain essential information. The processing and interpretation of such information require specialized algorithms. Off-the-shelf OCR components cannot process this information reliably. Therefore, an essential step in document analysis pipelines is to detect these graphical components. It leads to a high-level conceptual understanding of the documents that makes digitization of documents viable. Since the advent of deep learning, the performance of deep learning-based object detection has improved many folds. In this work, we outline and summarize the deep learning approaches for detecting graphical page objects in the document images. Therefore, we discuss the most relevant deep learning-based approaches and state-of-the-art graphical page object detection in document images. This work provides a comprehensive understanding of the current state-of-the-art and related challenges. Furthermore, we discuss leading datasets along with the quantitative evaluation. Moreover, it discusses briefly the promising directions that can be utilized for further improvements.

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Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning

10.1101/275867 ◽

2018 ◽

Cited By ~ 13

Author(s):

Alexey A. Shvets ◽

Alexander Rakhlin ◽

Alexandr A. Kalinin ◽

Vladimir I. Iglovikov

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Semantic Segmentation ◽

Robot Assisted ◽

Automatic Instrument ◽

Binary Segmentation ◽

Video Feed ◽

Class Segmentation ◽

Segmentation Problem ◽

Robot Assisted Surgery

AbstractSemantic segmentation of robotic instruments is an important problem for the robot-assisted surgery. One of the main challenges is to correctly detect an instrument’s position for the tracking and pose estimation in the vicinity of surgical scenes. Accurate pixel-wise instrument segmentation is needed to address this challenge. In this paper we describe our deep learning-based approach for robotic instrument segmentation. Our approach demonstrates an improvement over the state-of-the-art results using several novel deep neural network architectures. It addressed the binary segmentation problem, where every pixel in an image is labeled as an instrument or background from the surgery video feed. In addition, we solve a multi-class segmentation problem, in which we distinguish between different instruments or different parts of an instrument from the background. In this setting, our approach outperforms other methods for automatic instrument segmentation thereby providing state-of-the-art results for these problems. The source code for our solution is made publicly available.

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DeepTable: a permutation invariant neural network for table orientation classification

Data Mining and Knowledge Discovery ◽

10.1007/s10618-020-00711-x ◽

2020 ◽

Vol 34 (6) ◽

pp. 1963-1983

Author(s):

Maryam Habibi ◽

Johannes Starlinger ◽

Ulf Leser

Keyword(s):

Deep Neural Networks ◽

State Of The Art ◽

Superior Performance ◽

Web Pages ◽

Specific Information ◽

Silver Standard ◽

Present Information ◽

Previous State ◽

Novel Method

Abstract Tables are a common way to present information in an intuitive and concise manner. They are used extensively in media such as scientific articles or web pages. Automatically analyzing the content of tables bears special challenges. One of the most basic tasks is determination of the orientation of a table: In column tables, columns represent one entity with the different attribute values present in the different rows; row tables are vice versa, and matrix tables give information on pairs of entities. In this paper, we address the problem of classifying a given table into one of the three layouts horizontal (for row tables), vertical (for column tables), and matrix. We describe DeepTable, a novel method based on deep neural networks designed for learning from sets. Contrary to previous state-of-the-art methods, this basis makes DeepTable invariant to the permutation of rows or columns, which is a highly desirable property as in most tables the order of rows and columns does not carry specific information. We evaluate our method using a silver standard corpus of 5500 tables extracted from biomedical articles where the layout was determined heuristically. DeepTable outperforms previous methods in both precision and recall on our corpus. In a second evaluation, we manually labeled a corpus of 300 tables and were able to confirm DeepTable to reach superior performance in the table layout classification task. The codes and resources introduced here are available at https://github.com/Marhabibi/DeepTable.

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Deep learning for brain disorders: from data processing to disease treatment

Briefings in Bioinformatics ◽

10.1093/bib/bbaa310 ◽

2020 ◽

Author(s):

Ninon Burgos ◽

Simona Bottani ◽

Johann Faouzi ◽

Elina Thibeau-Sutre ◽

Olivier Colliot

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Language Processing ◽

State Of The Art ◽

Imaging Genetics ◽

Environmental Data ◽

Brain Disorders ◽

Disease Treatment ◽

Clinical Routine

Abstract In order to reach precision medicine and improve patients’ quality of life, machine learning is increasingly used in medicine. Brain disorders are often complex and heterogeneous, and several modalities such as demographic, clinical, imaging, genetics and environmental data have been studied to improve their understanding. Deep learning, a subpart of machine learning, provides complex algorithms that can learn from such various data. It has become state of the art in numerous fields, including computer vision and natural language processing, and is also growingly applied in medicine. In this article, we review the use of deep learning for brain disorders. More specifically, we identify the main applications, the concerned disorders and the types of architectures and data used. Finally, we provide guidelines to bridge the gap between research studies and clinical routine.

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