Towards robots with geologist eyes? Computer vision and Deep Learning approaches to field samples analysis

Automation and robotics raise growing interests in the mining industry. If not already a reality, it is no more science fiction to imagine autonomous robots routinely participating in the exploration and extraction of mineral raw materials in the near future. Among the various scientific and technical issues to be addressed towards this objective, this study focuses on the automation of real-time characterisation of rock images captured on the field, either to discriminate rock types and mineral species or to detect small elements such as mineral grains or metallic nuggets. To do so, we investigate the potential of methods from the Computer Vision community, a subfield of Artificial Intelligence dedicated to image processing. In particular, we aim at assessing the potential of Deep Learning approaches and convolutional neuronal networks (CNN) for the analysis of field samples pictures, highlighting key challenges before an industrial use in operational contexts.In a first initiative, we appraise Deep Learning methods to classify photographs of macroscopic rock samples between 12 lithological families. Using the architecture of reference CNN and a collection of 2700 images, we achieve a prediction accuracy above 90% for new pictures of good photographic quality. Nonetheless we then seek to improve the robustness of the method for on-the-fly field photographs. To do so, we train an additional CNN to automatically separate the rock sample from the background, with a detection algorithm. We also introduce a more sophisticated classification method combining a set of several CNN with a decision tree. The CNN are specifically trained to recognise petrological features such as textures, structures or mineral species, while the decision tree mimics the naturalist methodology for lithological identification.In a second initiative, we evaluate Deep Learning techniques to spot and delimitate specific elements in finer-scale images. We use a data set of carbonate thin sections with various species of microfossils. The data comes from a sedimentology study but analogies can be drawn with igneous geology use cases. We train four state-of-the-art Deep Learning methods for object detection with a limited data set of 15 annotated images. The results on 130 other thin section images are then qualitatively assessed by expert geologists, and precisions and inference times quantitatively measured. The four models show good capabilities in detecting and categorising the microfossils. However differences in accuracy and performance are underlined, leading to recommendations for comparable projects in a mining context.Altogether, this study illustrates the power of Computer Vision and Deep Learning approaches to automate rock image analysis. However, to make the most of these technologies in mining activities, stimulating research opportunities lies in adapting the algorithms to the geological use cases, embedding as much geological knowledge as possible in the statistical models, and mitigating the number of training data to be manually interpreted beforehand.&#160; &#160;

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Deep Learning Approaches for Whiteboard Image Quality Enhancement

Color and Imaging Conference ◽

10.2352/j.imagingsci.technol.2019.63.4.040404 ◽

2019 ◽

Vol 2019 (1) ◽

pp. 360-368

Author(s):

Mekides Assefa Abebe ◽

Jon Yngve Hardeberg

Keyword(s):

Deep Learning ◽

Image Quality ◽

Image Data ◽

Quality Enhancement ◽

Network Architectures ◽

Learning Approaches ◽

Data Set ◽

Image Quality Enhancement ◽

Processing Techniques ◽

White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

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A Review of Computer-Aided Expert Systems for Breast Cancer Diagnosis

Cancers ◽

10.3390/cancers13112764 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2764

Author(s):

Xin Yu Liew ◽

Nazia Hameed ◽

Jeremie Clos

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Deep Learning ◽

Main Process ◽

Learning Approaches ◽

Learning Methods ◽

Advantages And Disadvantages ◽

Computer Aided ◽

Conventional Methods ◽

The Impact

A computer-aided diagnosis (CAD) expert system is a powerful tool to efficiently assist a pathologist in achieving an early diagnosis of breast cancer. This process identifies the presence of cancer in breast tissue samples and the distinct type of cancer stages. In a standard CAD system, the main process involves image pre-processing, segmentation, feature extraction, feature selection, classification, and performance evaluation. In this review paper, we reviewed the existing state-of-the-art machine learning approaches applied at each stage involving conventional methods and deep learning methods, the comparisons within methods, and we provide technical details with advantages and disadvantages. The aims are to investigate the impact of CAD systems using histopathology images, investigate deep learning methods that outperform conventional methods, and provide a summary for future researchers to analyse and improve the existing techniques used. Lastly, we will discuss the research gaps of existing machine learning approaches for implementation and propose future direction guidelines for upcoming researchers.

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Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Entropy ◽

10.3390/e23060667 ◽

2021 ◽

Vol 23 (6) ◽

pp. 667

Author(s):

Wei Chen ◽

Qiang Sun ◽

Xiaomin Chen ◽

Gangcai Xie ◽

Huiqun Wu ◽

...

Keyword(s):

Neural Network ◽

Systematic Review ◽

Deep Learning ◽

Heart Sounds ◽

Learning Approaches ◽

Automated Classification ◽

Learning Methods ◽

Artificial Intelligence Technology ◽

Medical Big Data ◽

Effective Models

The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.

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Deep Learning Based Classification of Time Series of Chaotic Systems over Graphic Images

10.21203/rs.3.rs-1138927/v1 ◽

2021 ◽

Author(s):

Süleyman UZUN ◽

Sezgin KAÇAR ◽

Burak ARICIOĞLU

Keyword(s):

Time Series ◽

Deep Learning ◽

Transfer Learning ◽

Chaotic Systems ◽

Initial Conditions ◽

Step Size ◽

Learning Methods ◽

Data Set ◽

Graphic Images ◽

Different Chaotic Systems

Abstract In this study, for the first time in the literature, identification of different chaotic systems by classifying graphic images of their time series with deep learning methods is aimed. For this purpose, a data set is generated that consists of the graphic images of time series of the most known three chaotic systems: Lorenz, Chen, and Rossler systems. The time series are obtained for different parameter values, initial conditions, step size and time lengths. After generating the data set, a high-accuracy classification is performed by using transfer learning method. In the study, the most accepted deep learning models of the transfer learning methods are employed. These models are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet101, DenseNet201, ShuffleNet and GoogLeNet. As a result of the study, classification accuracy is found between 96% and 97% depending on the problem. Thus, this study makes association of real time random signals with a mathematical system possible.

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Analysis of Kinase Inhibitors and Druggability of Kinase-Targets Using Machine Learning Techniques

Pattern Discovery Using Sequence Data Mining ◽

10.4018/978-1-61350-056-9.ch009 ◽

2012 ◽

pp. 155-165

Author(s):

S. Prasanthi ◽

S.Durga Bhavani ◽

T. Sobha Rani ◽

Raju S. Bapi

Keyword(s):

Machine Learning ◽

Decision Tree ◽

Kinase Inhibitors ◽

Kinase Inhibitor ◽

Classification Problem ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Decision Tree Classifier ◽

Data Set ◽

Learning Techniques

Vast majority of successful drugs or inhibitors achieve their activity by binding to, and modifying the activity of a protein leading to the concept of druggability. A target protein is druggable if it has the potential to bind the drug-like molecules. Hence kinase inhibitors need to be studied to understand the specificity of a kinase inhibitor in choosing a particular kinase target. In this paper we focus on human kinase drug target sequences since kinases are known to be potential drug targets. Also we do a preliminary analysis of kinase inhibitors in order to study the problem in the protein-ligand space in future. The identification of druggable kinases is treated as a classification problem in which druggable kinases are taken as positive data set and non-druggable kinases are chosen as negative data set. The classification problem is addressed using machine learning techniques like support vector machine (SVM) and decision tree (DT) and using sequence-specific features. One of the challenges of this classification problem is due to the unbalanced data with only 48 druggable kinases available against 509 non-drugggable kinases present at Uniprot. The accuracy of the decision tree classifier obtained is 57.65 which is not satisfactory. A two-tier architecture of decision trees is carefully designed such that recognition on the non-druggable dataset also gets improved. Thus the overall model is shown to achieve a final performance accuracy of 88.37. To the best of our knowledge, kinase druggability prediction using machine learning approaches has not been reported in literature.

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Fashion Product Classification through Deep Learning and Computer Vision

Applied Sciences ◽

10.3390/app9071385 ◽

2019 ◽

Vol 9 (7) ◽

pp. 1385 ◽

Cited By ~ 4

Author(s):

Luca Donati ◽

Eleonora Iotti ◽

Giulio Mordonini ◽

Andrea Prati

Keyword(s):

Image Processing ◽

Computer Vision ◽

Feature Extraction ◽

Deep Learning ◽

Template Matching ◽

Learning Approaches ◽

Visual Classification ◽

Product Classification ◽

Processing Techniques

Visual classification of commercial products is a branch of the wider fields of object detection and feature extraction in computer vision, and, in particular, it is an important step in the creative workflow in fashion industries. Automatically classifying garment features makes both designers and data experts aware of their overall production, which is fundamental in order to organize marketing campaigns, avoid duplicates, categorize apparel products for e-commerce purposes, and so on. There are many different techniques for visual classification, ranging from standard image processing to machine learning approaches: this work, made by using and testing the aforementioned approaches in collaboration with Adidas AG™, describes a real-world study aimed at automatically recognizing and classifying logos, stripes, colors, and other features of clothing, solely from final rendering images of their products. Specifically, both deep learning and image processing techniques, such as template matching, were used. The result is a novel system for image recognition and feature extraction that has a high classification accuracy and which is reliable and robust enough to be used by a company like Adidas. This paper shows the main problems and proposed solutions in the development of this system, and the experimental results on the Adidas AG™ dataset.

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Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches

Energies ◽

10.3390/en12142692 ◽

2019 ◽

Vol 12 (14) ◽

pp. 2692 ◽

Cited By ~ 12

Author(s):

Juncheng Zhu ◽

Zhile Yang ◽

Monjur Mourshed ◽

Yuanjun Guo ◽

Yimin Zhou ◽

...

Keyword(s):

Deep Learning ◽

Comparative Study ◽

Electric Vehicle ◽

Electric Vehicles ◽

Load Forecasting ◽

Learning Approaches ◽

Short Term ◽

Learning Methods ◽

Electric Vehicle Charging ◽

Vehicle Charging

Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model.

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Deep learning for drug–drug interaction extraction from the literature: a review

Briefings in Bioinformatics ◽

10.1093/bib/bbz087 ◽

2019 ◽

Vol 21 (5) ◽

pp. 1609-1627 ◽

Cited By ~ 5

Author(s):

Tianlin Zhang ◽

Jiaxu Leng ◽

Ying Liu

Keyword(s):

Deep Learning ◽

Drug Research ◽

Drug Effects ◽

Learning Technologies ◽

Biomedical Literature ◽

Learning Approaches ◽

Learning Methods ◽

Feature Representations ◽

Advantages And Disadvantages ◽

Interaction Extraction

AbstractDrug–drug interactions (DDIs) are crucial for drug research and pharmacovigilance. These interactions may cause adverse drug effects that threaten public health and patient safety. Therefore, the DDIs extraction from biomedical literature has been widely studied and emphasized in modern biomedical research. The previous rules-based and machine learning approaches rely on tedious feature engineering, which is labourious, time-consuming and unsatisfactory. With the development of deep learning technologies, this problem is alleviated by learning feature representations automatically. Here, we review the recent deep learning methods that have been applied to the extraction of DDIs from biomedical literature. We describe each method briefly and compare its performance in the DDI corpus systematically. Next, we summarize the advantages and disadvantages of these deep learning models for this task. Furthermore, we discuss some challenges and future perspectives of DDI extraction via deep learning methods. This review aims to serve as a useful guide for interested researchers to further advance bioinformatics algorithms for DDIs extraction from the literature.

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Comparison of Object Detection Methods for Corn Damage Assessment Using Deep Learning

Transactions of the ASABE ◽

10.13031/trans.13791 ◽

2020 ◽

Vol 63 (6) ◽

pp. 1969-1980

Author(s):

Ali Hamidisepehr ◽

Seyed V. Mirnezami ◽

Jason K. Ward

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Object Detection ◽

Damage Detection ◽

Damage Identification ◽

Severe Weather ◽

Crop Damage ◽

Growth Stages ◽

List Type ◽

Data Set

HighlightsCorn damage detection was possible using advanced deep learning and computer vision techniques trained with images of simulated corn lodging.RetinaNet and YOLOv2 both worked well at identifying regions of lodged corn.Automating crop damage identification could provide useful information to producers and other stakeholders from visual-band UAS imagery.Abstract. Severe weather events can cause large financial losses to farmers. Detailed information on the location and severity of damage will assist farmers, insurance companies, and disaster response agencies in making wise post-damage decisions. The goal of this study was a proof-of-concept to detect areas of damaged corn from aerial imagery using computer vision and deep learning techniques. A specific objective was to compare existing object detection algorithms to determine which is best suited for corn damage detection. Simulated corn lodging was used to create a training and analysis data set. An unmanned aerial system equipped with an RGB camera was used for image acquisition. Three popular object detectors (Faster R-CNN, YOLOv2, and RetinaNet) were assessed for their ability to detect damaged areas. Average precision (AP) was used to compare object detectors. RetinaNet and YOLOv2 demonstrated robust capability for corn damage identification, with AP ranging from 98.43% to 73.24% and from 97.0% to 55.99%, respectively, across all conditions. Faster R-CNN did not perform as well as the other two models, with AP between 77.29% and 14.47% for all conditions. Detecting corn damage at later growth stages was more difficult for all three object detectors. Keywords: Computer vision, Faster R-CNN, RetinaNet, Severe weather, Smart farming, YOLO.

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A survey on Deep Learning Based Eye Gaze Estimation Methods

Journal of Innovative Image Processing - December 2019 ◽

10.36548/jiip.2021.3.003 ◽

2021 ◽

Vol 3 (3) ◽

pp. 190-207

Author(s):

S. K. B. Sangeetha

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Near Infrared ◽

Eye Gaze ◽

Infrared Image ◽

Estimation Methods ◽

Learning Technology ◽

Learning Approaches ◽

Gaze Tracking ◽

Inference Models

In recent years, deep-learning systems have made great progress, particularly in the disciplines of computer vision and pattern recognition. Deep-learning technology can be used to enable inference models to do real-time object detection and recognition. Using deep-learning-based designs, eye tracking systems could determine the position of eyes or pupils, regardless of whether visible-light or near-infrared image sensors were utilized. For growing electronic vehicle systems, such as driver monitoring systems and new touch screens, accurate and successful eye gaze estimates are critical. In demanding, unregulated, low-power situations, such systems must operate efficiently and at a reasonable cost. A thorough examination of the different deep learning approaches is required to take into consideration all of the limitations and opportunities of eye gaze tracking. The goal of this research is to learn more about the history of eye gaze tracking, as well as how deep learning contributed to computer vision-based tracking. Finally, this research presents a generalized system model for deep learning-driven eye gaze direction diagnostics, as well as a comparison of several approaches.

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