scholarly journals Detection and annotation of plant organs from digitised herbarium scans using deep learning

2020 ◽  
Vol 8 ◽  
Author(s):  
Sohaib Younis ◽  
Marco Schmidt ◽  
Claus Weiland ◽  
Stefan Dressler ◽  
Bernhard Seeger ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Recognition ◽  
Herbarium Specimens ◽  
Plant Organs ◽  
Detection Model ◽  
Plant Organ ◽  
Large Numbers ◽  
Bounding Boxes ◽  
Advanced Computer ◽  
Extract Information

As herbarium specimens are increasingly becoming digitised and accessible in online repositories, advanced computer vision techniques are being used to extract information from them. The presence of certain plant organs on herbarium sheets is useful information in various scientific contexts and automatic recognition of these organs will help mobilise such information. In our study, we use deep learning to detect plant organs on digitised herbarium specimens with Faster R-CNN. For our experiment, we manually annotated hundreds of herbarium scans with thousands of bounding boxes for six types of plant organs and used them for training and evaluating the plant organ detection model. The model worked particularly well on leaves and stems, while flowers were also present in large numbers in the sheets, but were not equally well recognised.

scholarly journals A Pipeline for Deep Learning with Specimen Images in iDigBio - Applying and Generalizing an Examination of Mercury Use in Preparing Herbarium Specimens

2018 ◽  
Vol 2 ◽  
pp. e25699
Author(s):  
Matthew Collins ◽  
Gaurav Yeole ◽  
Paul Frandsen ◽  
Rebecca Dikow ◽  
Sylvia Orli ◽  
...  
Keyword(s):  
Deep Learning ◽  
Storage System ◽  
Research Result ◽  
Open Data ◽  
Data Access ◽  
Herbarium Specimens ◽  
Large Numbers ◽  
The Neural Networks ◽  
Graphical Processing ◽  
Advanced Computing

iDigBio Matsunaga et al. 2013 currently references over 22 million media files, and stores approximately 120 terabytes worth of those media files co-located with our compute infrastructure. Using these images for scientific research is a logistical and technical challenge. Transferring large numbers of images requires programming skill, bandwidth, and storage space. While simple image transformations such as resizing and generating histograms are approachable on desktops and laptops, the neural networks commonly used for learning from images require server-based graphical processing units (GPUs) to run effectively. Using the GUODA (Global Unified Open Data Access) infrastructure, we have built a model pipeline for applying user-defined processing to any subset of the images stored in iDigBio. This pipeline is run on servers located in the Advanced Computing and Information Systems lab (ACIS) alongside the iDigBio storage system. We use Apache Spark, the Hadoop File System (HDFS), and Mesos to perform the processing. We have placed a Jupyter notebook server in front of this architecture which provides an easy environment with deep learning libraries for Python already loaded for end users to write their own models. Users can access the stored data and images and manipulate them according to their requirements and make their work publicly available on GitHub. As an example of how this pipeline can be used in research, we applied a neural network developed at the Smithsonian Institution to identify herbarium sheets that were prepared with hazardous mercury containing solutions Schuettpelz et al. 2017. The model was trained with Smithsonian resources on their images and transferred to the GUODA infrastructure hosted at ACIS which also houses iDigBio. We then applied this model to additional images in iDigBio to classify them to illustrate the application of these techniques to broad image corpora potentially to notify other data publishers of contamination. We present the results of this classification not as a verified research result, but as an example of the collaborative and scalable workflows this pipeline and infrastructure enable.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

A unified framework for automated person re-indentification

2020 ◽  
Vol 71 (7) ◽  
pp. 868-880
Author(s):  
Nguyen Hong-Quan ◽  
Nguyen Thuy-Binh ◽  
Tran Duc-Long ◽  
Le Thi-Lan
Keyword(s):  
Deep Learning ◽  
Video Analysis ◽  
Camera Network ◽  
Unified Framework ◽  
Person Detection ◽  
Practical Applications ◽  
Detection And Tracking ◽  
Analysis System ◽  
Bounding Boxes

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

scholarly journals U-Infuse: Democratization of Customizable Deep Learning for Object Detection

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2611
Author(s):  
Andrew Shepley ◽  
Greg Falzon ◽  
Christopher Lawson ◽  
Paul Meek ◽  
Paul Kwan
Keyword(s):  
Deep Learning ◽  
Intellectual Property ◽  
Object Detection ◽  
Image Data ◽  
Learning Technologies ◽  
Training Data ◽  
Learning Models ◽  
Ecological Data ◽  
Single Class ◽  
Large Numbers

Image data is one of the primary sources of ecological data used in biodiversity conservation and management worldwide. However, classifying and interpreting large numbers of images is time and resource expensive, particularly in the context of camera trapping. Deep learning models have been used to achieve this task but are often not suited to specific applications due to their inability to generalise to new environments and inconsistent performance. Models need to be developed for specific species cohorts and environments, but the technical skills required to achieve this are a key barrier to the accessibility of this technology to ecologists. Thus, there is a strong need to democratize access to deep learning technologies by providing an easy-to-use software application allowing non-technical users to train custom object detectors. U-Infuse addresses this issue by providing ecologists with the ability to train customised models using publicly available images and/or their own images without specific technical expertise. Auto-annotation and annotation editing functionalities minimize the constraints of manually annotating and pre-processing large numbers of images. U-Infuse is a free and open-source software solution that supports both multiclass and single class training and object detection, allowing ecologists to access deep learning technologies usually only available to computer scientists, on their own device, customised for their application, without sharing intellectual property or sensitive data. It provides ecological practitioners with the ability to (i) easily achieve object detection within a user-friendly GUI, generating a species distribution report, and other useful statistics, (ii) custom train deep learning models using publicly available and custom training data, (iii) achieve supervised auto-annotation of images for further training, with the benefit of editing annotations to ensure quality datasets. Broad adoption of U-Infuse by ecological practitioners will improve ecological image analysis and processing by allowing significantly more image data to be processed with minimal expenditure of time and resources, particularly for camera trap images. Ease of training and use of transfer learning means domain-specific models can be trained rapidly, and frequently updated without the need for computer science expertise, or data sharing, protecting intellectual property and privacy.

scholarly journals Machine Learning-Based Malicious X.509 Certificates’ Detection

2021 ◽  
Vol 11 (5) ◽  
pp. 2164
Author(s):  
Jiaxin Li ◽  
Zhaoxin Zhang ◽  
Changyong Guo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ensemble Learning ◽  
Traffic Analysis ◽  
Learning Models ◽  
Detection Model ◽  
Analysis Tools ◽  
Average Accuracy ◽  
Machine Learning Models

X.509 certificates play an important role in encrypting the transmission of data on both sides under HTTPS. With the popularization of X.509 certificates, more and more criminals leverage certificates to prevent their communications from being exposed by malicious traffic analysis tools. Phishing sites and malware are good examples. Those X.509 certificates found in phishing sites or malware are called malicious X.509 certificates. This paper applies different machine learning models, including classical machine learning models, ensemble learning models, and deep learning models, to distinguish between malicious certificates and benign certificates with Verification for Extraction (VFE). The VFE is a system we design and implement for obtaining plentiful characteristics of certificates. The result shows that ensemble learning models are the most stable and efficient models with an average accuracy of 95.9%, which outperforms many previous works. In addition, we obtain an SVM-based detection model with an accuracy of 98.2%, which is the highest accuracy. The outcome indicates the VFE is capable of capturing essential and crucial characteristics of malicious X.509 certificates.

scholarly journals Correspondence Learning for Deep Multi-Modal Recognition and Fraud Detection

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 800
Author(s):  
Jongchan Park ◽  
Min-Hyun Kim ◽  
Dong-Geol Choi
Keyword(s):  
Deep Learning ◽  
Simple Technique ◽  
Detection Method ◽  
Fraud Detection ◽  
Complementary Information ◽  
Multiple Modalities ◽  
Vending Machines ◽  
Emotion Intensity ◽  
The Relationship ◽  
Extract Information

Deep learning-based methods have achieved good performance in various recognition benchmarks mostly by utilizing single modalities. As different modalities contain complementary information to each other, multi-modal based methods are proposed to implicitly utilize them. In this paper, we propose a simple technique, called correspondence learning (CL), which explicitly learns the relationship among multiple modalities. The multiple modalities in the data samples are randomly mixed among different samples. If the modalities are from the same sample (not mixed), then they have positive correspondence, and vice versa. CL is an auxiliary task for the model to predict the correspondence among modalities. The model is expected to extract information from each modality to check correspondence and achieve better representations in multi-modal recognition tasks. In this work, we first validate the proposed method in various multi-modal benchmarks including CMU Multimodal Opinion-Level Sentiment Intensity (CMU-MOSI) and CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) sentiment analysis datasets. In addition, we propose a fraud detection method using the learned correspondence among modalities. To validate this additional usage, we collect a multi-modal dataset for fraud detection using real-world samples for reverse vending machines.

scholarly journals Automated Counting Grains on the Rice Panicle Based on Deep Learning Method

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 281
Author(s):  
Ruoling Deng ◽  
Ming Tao ◽  
Xunan Huang ◽  
Kemoh Bangura ◽  
Qian Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Model Performance ◽  
Rice Breeding ◽  
Grain Number ◽  
Lighting Condition ◽  
Detection Model ◽  
Grain Moisture ◽  
Feature Pyramid ◽  
Moisture Conditions ◽  
Rice Panicles

Grain number per rice panicle, which directly determines grain yield, is an important agronomic trait for rice breeding and yield-related research. However, manually counting grains of rice per panicle is time-consuming, laborious, and error-prone. In this research, a grain detection model was proposed to automatically recognize and count grains on primary branches of a rice panicle. The model used image analysis based on deep learning convolutional neural network (CNN), by integrating the feature pyramid network (FPN) into the faster R-CNN network. The performance of the grain detection model was compared to that of the original faster R-CNN model and the SSD model, and it was found that the grain detection model was more reliable and accurate. The accuracy of the grain detection model was not affected by the lighting condition in which images of rice primary branches were taken. The model worked well for all rice branches with various numbers of grains. Through applying the grain detection model to images of fresh and dry branches, it was found that the model performance was not affected by the grain moisture conditions. The overall accuracy of the grain detection model was 99.4%. Results demonstrated that the model was accurate, reliable, and suitable for detecting grains of rice panicles with various conditions.

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