Macroscopic pest detection system for aromatic herbs in post-harvest stage using computer vision and neural networks techniques

Raspberries are fruit of great importance for human beings. Their products are segmented by quality. However, estimating raspberry quality is a manual process carried out at the reception of the fruit processing plant,and is thus exposed to factors that could distort the measurement. The agriculture industry has increased the use of deep learning (DL) in computer vision systems. Non-destructive and computer vision equipment and methods are proposed to solve the problem of estimating the quality of raspberries in a tray. To solve the issue of estimating the quality of raspberries in a picking tray, prototype equipment is developed to determine the quality of raspberry trays using computer vision techniques and convolutional neural networks from images captured in the visible RGB spectrum. The Faster R–CNN object-detection algorithm is used, and different pretrained CNN networks are evaluated as a backbone to develop the software for the developed equipment. To avoid imbalance in the dataset, an individual object-detection model is trained and optimized for each detection class. Finally, both hardware and software are effectively integrated. A conceptual test is performed in a real industrial scenario, thus achieving an automatic evaluation of the quality of the raspberry tray, in this way eliminating the intervention of the human expert and eliminating errors involved in visual analysis. Excellent results were obtained in the conceptual test performed, reaching in some cases precision of 100%, reducing the evaluation time per raspberry tray image to 30 s on average, allowing the evaluation of a larger and representative sample of the raspberry batch arriving at the processing plant.

Download Full-text

The machine training in problems of satellite images’s processing

Metrologiya ◽

10.32446/0132-4713.2020-4-15-37 ◽

2020 ◽

pp. 15-37

Author(s):

L. P. Bass ◽

Yu. A. Plastinin ◽

I. Yu. Skryabysheva

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Computer Vision ◽

Satellite Images ◽

Vision Systems ◽

Earth Remote Sensing ◽

Practical Applications ◽

Convolution Neural Networks ◽

Computer Vision Systems ◽

Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

Download Full-text

Algorithms for segmentation and recognition of objects on medical images based on chiarlet transformation and neural networks

Informatization and communication ◽

10.34219/2078-8320-2020-11-2-35-45 ◽

2020 ◽

pp. 35-45

Author(s):

Y.A. Hamad ◽

K.V. Simonov ◽

A.S. Kents

Keyword(s):

Neural Networks ◽

Image Processing ◽

Computer Vision ◽

Edge Detection ◽

Medical Images ◽

Classification Algorithms ◽

Visual Data ◽

Recognition Of Objects

The paper considers general approaches to image processing, analysis of visual data and computer vision. The main methods for detecting features and edges associated with these approaches are presented. A brief description of modern edge detection and classification algorithms suitable for isolating and characterizing the type of pathology in the lungs in medical images is also given.

Download Full-text

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666190114151255 ◽

2020 ◽

Vol 16 (5) ◽

pp. 584-591 ◽

Cited By ~ 1

Author(s):

Muhammad Hanif Ahmad Nizar ◽

Chow Khuen Chan ◽

Azira Khalil ◽

Ahmad Khairuddin Mohamed Yusof ◽

Khin Wee Lai

Keyword(s):

Neural Network ◽

Neural Networks ◽

Heart Disease ◽

Aortic Valve ◽

Real Time ◽

Convolutional Neural Networks ◽

Valvular Heart Disease ◽

Detection System ◽

Processing Unit ◽

Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

Download Full-text

Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks

Sensors ◽

10.3390/s21072538 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2538

Author(s):

Shuang Zhang ◽

Feng Liu ◽

Yuang Huang ◽

Xuedong Meng

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Spread Spectrum ◽

High Efficiency ◽

Detection System ◽

Direct Sequence Spread Spectrum ◽

Detection Performance ◽

Detection Methods ◽

Direct Sequence ◽

Artificial Neural

The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.

Download Full-text

Bipolar Morphological Neural Networks: Gate-Efficient Architecture for Computer Vision

IEEE Access ◽

10.1109/access.2021.3094484 ◽

2021 ◽

pp. 1-1

Author(s):

Elena E. Limonova ◽

Daniil M. Alfonso ◽

Dmitry P. Nikolaev ◽

Vladimir V. Arlazarov

Keyword(s):

Neural Networks ◽

Computer Vision ◽

Morphological Neural Networks

Download Full-text

Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality

npj Computational Materials ◽

10.1038/s41524-020-00487-0 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Rama K. Vasudevan ◽

Maxim Ziatdinov ◽

Lukas Vlcek ◽

Sergei V. Kalinin

Keyword(s):

Neural Networks ◽

Computer Vision ◽

Deep Learning ◽

Bayesian Methods ◽

Deep Neural Networks ◽

Applied Research ◽

Modern Science ◽

Generative Models ◽

Knowledge Development ◽

Physical Constraints

AbstractDeep neural networks (‘deep learning’) have emerged as a technology of choice to tackle problems in speech recognition, computer vision, finance, etc. However, adoption of deep learning in physical domains brings substantial challenges stemming from the correlative nature of deep learning methods compared to the causal, hypothesis driven nature of modern science. We argue that the broad adoption of Bayesian methods incorporating prior knowledge, development of solutions with incorporated physical constraints and parsimonious structural descriptors and generative models, and ultimately adoption of causal models, offers a path forward for fundamental and applied research.

Download Full-text

Deep learning-enabled medical computer vision

npj Digital Medicine ◽

10.1038/s41746-020-00376-2 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Andre Esteva ◽

Katherine Chou ◽

Serena Yeung ◽

Nikhil Naik ◽

Ali Madani ◽

...

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Computer Vision ◽

Deep Learning ◽

Medical Imaging ◽

Real World ◽

Recent Progress ◽

Medical Applications ◽

Modern Computer ◽

Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

Download Full-text