scholarly journals Analysis of the Functionality of the Feed Chain in Olive Pitting, Slicing and Stuffing Machines by IoT, Computer Vision and Neural Network Diagnosis

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1541
Author(s):  
Alberto Lucas Pascual ◽  
Antonio Madueño Luna ◽  
Manuel de Jódar Lázaro ◽  
José Miguel Molina Martínez ◽  
Antonio Ruiz Canales ◽  
...  
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Vision System ◽  
Operating Speed ◽  
Lighting System ◽  
Safety Issues ◽  
Optimal Functioning ◽  
Innovative Solution ◽  
Network Diagnosis ◽  
Small Parts

Olive pitting, slicing and stuffing machines (DRR in Spanish) are characterized by the fact that their optimal functioning is based on appropriate adjustments. Traditional systems are not completely reliable because their minimum error rate is 1–2%, which can result in fruit loss, since the pitting process is not infallible, and food safety issues can arise. Such minimum errors are impossible to remove through mechanical adjustments. In order to achieve this objective, an innovative solution must be provided in order to remove errors at operating speed rates over 2500 olives/min. This work analyzes the appropriate placement of olives in the pockets of the feed chain by using the following items: (1) An IoT System to control the DRR machine and the data analysis. (2) A computer vision system with an external shot camera and a LED lighting system, which takes a picture of every pocket passing in front of the camera. (3) A chip with a neural network for classification that, once trained, classifies between four possible pocket cases: empty, normal, incorrectly de-stoned olives at any angles (also known as a “boat”), and an anomalous case (foreign elements such as leafs, small branches or stones, two olives or small parts of olives in the same pocket). The main objective of this paper is to illustrate how with the use of a system based on IoT and a physical chip (NeuroMem CM1K, General Vision Inc.) with neural networks for sorting purposes, it is possible to optimize the functionality of this type of machine by remotely analyzing the data obtained. The use of classifying hardware allows it to work at the nominal operating speed for these machines. This would be limited if other classifying techniques based on software were used.

scholarly journals Advanced Object Detection in Bio-Medical X-Ray Images for Anomaly Detection and Recognition

2021 ◽  
Vol 12 (2) ◽  
pp. 93-110
Author(s):  
Garv Modwel ◽  
Anu Mehra ◽  
Nitin Rakesh ◽  
K. K. Mishra
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Vision System ◽  
Human Vision ◽  
Biomedical Image Processing ◽  
X Ray ◽  
Biomedical Image ◽  
X Ray Imaging ◽  
Magnetic Resonance Imaging Mri ◽  
Result Analysis

The human vision system is mimicked in the format of videos and images in the area of computer vision. As humans can process their memories, likewise video and images can be processed and perceptive with the help of computer vision technology. There is a broad range of fields that have great speculation and concepts building in the area of application of computer vision, which includes automobile, biomedical, space research, etc. The case study in this manuscript enlightens one about the innovation and future scope possibilities that can start a new era in the biomedical image-processing sector. A pre-surgical investigation can be perused with the help of the proposed technology that will enable the doctors to analyses the situations with deeper insight. There are different types of biomedical imaging such as magnetic resonance imaging (MRI), computerized tomographic (CT) scan, x-ray imaging. The focused arena of the proposed research is x-ray imaging in this subset. As it is always error-prone to do an eyeball check for a human when it comes to the detailing. The same applied to doctors. Subsequently, they need different equipment for related technologies. The methodology proposed in this manuscript analyses the details that may be missed by an expert doctor. The input to the algorithm is the image in the format of x-ray imaging; eventually, the output of the process is a label on the corresponding objects in the test image. The tool used in the process also mimics the human brain neuron system. The proposed method uses a convolutional neural network to decide on the labels on the objects for which it interprets the image. After some pre-processing the x-ray images, the neural network receives the input to achieve an efficient performance. The result analysis is done that gives a considerable performance in terms of confusion factor that is represented in terms of percentage. At the end of the narration of the manuscript, future possibilities are being traces out to the limelight to conduct further research.

scholarly journals A Computer Vision System Based on Majority-Voting Ensemble Neural Network for the Automatic Classification of Three Chickpea Varieties

Foods ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 113 ◽  
Author(s):  
Razieh Pourdarbani ◽  
Sajad Sabzi ◽  
Davood Kalantari ◽  
José Luis Hernández-Hernández ◽  
Juan Ignacio Arribas
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Artificial Neural Network ◽  
Vision System ◽  
Color Space ◽  
Majority Voting ◽  
Hsi Color Space ◽  
Artificial Neural ◽  
Hybrid Artificial Neural Network

Since different varieties of crops have specific applications, it is therefore important to properly identify each cultivar, in order to avoid fake varieties being sold as genuine, i.e., fraud. Despite that properly trained human experts might accurately identify and classify crop varieties, computer vision systems are needed since conditions such as fatigue, reproducibility, and so on, can influence the expert’s judgment and assessment. Chickpea (Cicer arietinum L.) is an important legume at the world-level and has several varieties. Three chickpea varieties with a rather similar visual appearance were studied here: Adel, Arman, and Azad chickpeas. The purpose of this paper is to present a computer vision system for the automatic classification of those chickpea varieties. First, segmentation was performed using an Hue Saturation Intensity (HSI) color space threshold. Next, color and textural (from the gray level co-occurrence matrix, GLCM) properties (features) were extracted from the chickpea sample images. Then, using the hybrid artificial neural network-cultural algorithm (ANN-CA), the sub-optimal combination of the five most effective properties (mean of the RGB color space components, mean of the HSI color space components, entropy of GLCM matrix at 90°, standard deviation of GLCM matrix at 0°, and mean third component in YCbCr color space) were selected as discriminant features. Finally, an ANN-PSO/ACO/HS majority voting (MV) ensemble methodology merging three different classifier outputs, namely the hybrid artificial neural network-particle swarm optimization (ANN-PSO), hybrid artificial neural network-ant colony optimization (ANN-ACO), and hybrid artificial neural network-harmonic search (ANN-HS), was used. Results showed that the ensemble ANN-PSO/ACO/HS-MV classifier approach reached an average classification accuracy of 99.10 ± 0.75% over the test set, after averaging 1000 random iterations.

Computer Vision and Pattern Recognition in Forensic Science

2019 ◽  
pp. 66-74
Author(s):  
Dmitrii Bakhteev
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Vision System ◽  
Modern Technology ◽  
Training Dataset ◽  
Network Training ◽  
Application Systems ◽  
Current State ◽  
History Of ◽  
Artificial Neural

The article discusses computer vision as a modern technology of automatic processing of graphic images, analyzes the relations between the terms «computer vision» and «machine vision». History of development of this technology is described, it occurred because of improvements in both computer technology and software. The computerization of forensic activities boils down to three areas: speeding up, simplifying, and improving the efficiency of information processing. A schema of a typical computer vision system is given, the possibility of using systems based on artificial neural networks for image analysis is considered. The current state of computer vision application systems and the possibility of its application in order to solve the problems of criminal justice are analyzed. The main areas of application of computer vision in forensic activities are identification of a person on the basis of his appearance, both during operational identification of a person and portrait examinations, photo and video examinations; quantitative assessment of objects in the image (for example, in case of calculating mass events’ participants); at preliminary and expert research of documents and their requisites; in functioning of criminal registration systems. Criteria and technical conditions for sampling signatures for creation of a training dataset for a neural network are given, the basics of developing an artificial neural network recognizing signs of signatures’ forgery is analyzed, which include three steps: creating a training dataset, adjusting weights and training priorities, testing the quality of network training.

On computer vision algorithms for searching breaks in meshed fencing constructions

2019 ◽  
Author(s):  
К.Н. Дубровин ◽  
А.С. Смагин
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Software Package ◽  
Vision System ◽  
Network Module ◽  
Noise Elimination ◽  
Efficient Detection ◽  
Otsu Method ◽  
Color Restoration ◽  
Python Programming

Рассмотрены вопросы автоматизированного мониторинга состояния сетчатых ограждающих конструкций, которые используются на предприятиях, производящих морские биоресурсы. Предложен алгоритм выделения порывов сетчатых ограждений в подводных условиях с применением методов компьютерного зрения, который реализован в виде комплекса программ на языке Python. Приведены результаты работы программного комплекса. Показано, что методы компьютерного зрения эффективно справляются с определением целостности ячеек сети на слабои среднезашумленных изображениях. Для работы в более сложных оптических условиях в состав программного комплекса предложено включить нейросетевой модуль. Purpose. The paper addresses image processing algorithms for the computer vision system of an autonomous uninhabited underwater vehicle, which automatically monitors the state of the mesh fence and thereby exclude the presence of a person in an aggressive underwater environment. Methodology. The MultiScale Retinex with Color Restoration algorithm and the Otsu method were implemented using the Python programming language to preprocess and filter the image. Methods from OpenCV computer vision library were used to detect damage to the mesh fence. Results. An algorithm is proposed for highlighting the impulses of mesh fencing in underwater conditions using computer vision methods implemented by the Python software. The software implementation results are provided. It is shown that computer vision methods effectively cope with determining the integrity of network cells in weakly and medium-noisy images. To work in more complex optical conditions, it is proposed to include a neural network module in the software package. Findings. The analysis of the results of the software package showed that it successfully copes with the classification of network cells in clean images. However, the transformations carried out at the pre-processing stage are not enough to complete noise elimination. In this regard, this study will continue to improve and expand the functionality of the software package. The result of the study will be a software package with a neural network module for full filtering of the external background and efficient detection of the mesh fence problem areas.

scholarly journals An Efficient Convolutional Neural Network Computer Vision System to Prevent Sudden Infant Death Syndrome

2020 ◽  
Author(s):  
Vivek S Bharati
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Sudden Infant Death Syndrome ◽  
Infant Death ◽  
Vision System ◽  
Sudden Infant Death ◽  
Activation Function ◽  
Sudden Infant ◽  
Computer Vision System ◽  
Sleeping Position

Sudden Infant Death Syndrome (SIDS) causes infants under one year of age to die inexplicably. One of the most important external factors responsible for the syndrome, called an ‘outside stressor’, is the sleeping position of the baby. When the baby sleeps on the stomach with face down, the risk of SIDS occurring is very high. We propose a Convolutional Neural Network (CNN) based computer vision system that can alert caregivers on their mobile phones within a few seconds of the baby moving to a hazardous face-down sleeping position. The model processes real-time image feeds with a single efficient forward pass. It has a low computational load and a low memory footprint. This would allow it to be embedded in low power edge devices such as crib cameras. Processing at the edge would also alleviate privacy concerns in sending images into the network. The CNN architecture is composed of multiple sets of processing units, each unit containing a 2D convolutional layer with the Rectified Linear Unit activation function followed by a Max Pooling layer. The final layer in the architecture is a fully connected dense layer with the Sigmoid activation function and outputs three classes of sleeping position indicators. The seed corpus for the training dataset was generated from realistic baby dolls with diverse racial mix in three sleeping positions (face-up, turning, face-down). These seed images were used to generate additional images by applying various image transformations. We experimented with various numbers of convolutional processing units and dense layers as well as the number of convolutional kernels to arrive at the optimal production configuration. We observed a consistently high accuracy of detection of sleeping position changes to turning and face-down positions with a trend towards even higher accuracies with caregiver feedback. Therefore, this system is a viable candidate for consideration as a non-intrusive technology to assist in preventing the Sudden Infant Death Syndrome.

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