Application of convolutional neural network to acquisition of clear images for objects with large vertical size in stereo light microscope vision system

2019 ◽  
Vol 83 (2) ◽  
pp. 140-147
Author(s):  
Junhua Zhang ◽  
Xuefang Li ◽  
Yufeng Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Light Microscope ◽  
Vision System ◽  
Vertical Size

scholarly journals Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models

Agriculture ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1111
Author(s):  
Abozar Nasirahmadi ◽  
Ulrike Wilczek ◽  
Oliver Hensel
Keyword(s):  
Neural Network ◽  
Sugar Beet ◽  
Convolutional Neural Network ◽  
Imaging System ◽  
Vision System ◽  
Mechanical Damage ◽  
Image Data ◽  
Network Models ◽  
Neural Network Models ◽  
Convolutional Network

Mechanical damages of sugar beet during harvesting affects the quality of the final products and sugar yield. The mechanical damage of sugar beet is assessed randomly by operators of harvesters and can depend on the subjective opinion and experience of the operator due to the complexity of the harvester machines. Thus, the main aim of this study was to determine whether a digital two-dimensional imaging system coupled with convolutional neural network (CNN) techniques could be utilized to detect visible mechanical damage in sugar beet during harvesting in a harvester machine. In this research, various detector models based on the CNN, including You Only Look Once (YOLO) v4, region-based fully convolutional network (R-FCN) and faster regions with convolutional neural network features (Faster R-CNN) were developed. Sugar beet image data during harvesting from a harvester in different farming conditions were used for training and validation of the proposed models. The experimental results showed that the YOLO v4 CSPDarknet53 method was able to detect damage in sugar beet with better performance (recall, precision and F1-score of about 92, 94 and 93%, respectively) and higher speed (around 29 frames per second) compared to the other developed CNNs. By means of a CNN-based vision system, it was possible to automatically detect sugar beet damage within the sugar beet harvester machine.

scholarly journals Training dataset generation for automatic registration of a duplicate bridge game

2021 ◽  
Author(s):  
Piotr Wzorek ◽  
Tomasz Kryjak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Vision System ◽  
Automatic Generation ◽  
Detection Task ◽  
Deep Convolutional Neural Network ◽  
Training Dataset ◽  
Automatic Registration ◽  
Playing Card

This paper presents a method for automatic generation of a training dataset for a deep convolutional neural network used for playing card detection. The solution allows to skip the time-consuming processes of manual image collecting and labelling recognised objects. The YOLOv4 network trained on the generated dataset achieved an efficiency of 99.8% in the cards detection task. The proposed method is a part of a project that aims to automate the process of broadcasting duplicate bridge competitions using a vision system and neural networks.

UAV Navigation System Autonomous Correction Algorithm Based on Road and River Network Recognition

2020 ◽  
Vol 28 (3) ◽  
pp. 32-42
Author(s):  
A.P. Tanchenko ◽  
◽  
A.M. Fedulin ◽  
R.R. Bikmaev ◽  
R.N. Sadekov ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Vision ◽  
Convolutional Neural Network ◽  
Navigation System ◽  
Vision System ◽  
River Network ◽  
Machine Vision System ◽  
Topographic Map ◽  
Correction Algorithm ◽  
Uav Navigation

The paper considers an original autonomous correction algorithm for UAV navigation system based on comparison between terrain images obtained by onboard machine vision system and vector topographic map images. Comparison is performed by calculating the homography of vision system images segmented using the convolutional neural network and the vector map images. The presented results of mathematical and flight experiments confirm the algorithm effectiveness for navigation applications.

scholarly journals Application of Deep Learning Networks to Segmentation of Surface of Railway Tracks

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4065
Author(s):  
Piotr Bojarczak ◽  
Waldemar Nowakowski
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Vision System ◽  
Gaussian Function ◽  
Railway Track ◽  
Learning Networks ◽  
Improve Performance ◽  
Speed Up ◽  
Visual Geometry

The article presents a vision system for detecting elements of railway track. Four types of fasteners, wooden and concrete sleepers, rails, and turnouts can be recognized by our system. In addition, it is possible to determine the degree of sleeper ballast coverage. Our system is also able to work when the track is moderately covered by snow. We used a Fully Convolutional Neural Network with 8 times upsampling (FCN-8) to detect railway track elements. In order to speed up training and improve performance of the model, a pre-trained deep convolutional neural network developed by Oxford’s Visual Geometry Group (VGG16) was used as a framework for our system. We also verified the invariance of our system to changes in brightness. To do this, we artificially varied the brightness of images. We performed two types of tests. In the first test, we changed the brightness by a constant value for the whole analyzed image. In the second test, we changed the brightness according to a predefined distribution corresponding to Gaussian function.

scholarly journals Training dataset generation for automatic registration of a duplicate bridge game

2021 ◽  
Author(s):  
Piotr Wzorek ◽  
Tomasz Kryjak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Vision System ◽  
Automatic Generation ◽  
Detection Task ◽  
Deep Convolutional Neural Network ◽  
Training Dataset ◽  
Automatic Registration ◽  
Playing Card

This paper presents a method for automatic generation of a training dataset for a deep convolutional neural network used for playing card detection. The solution allows to skip the time-consuming processes of manual image collecting and labelling recognised objects. The YOLOv4 network trained on the generated dataset achieved an efficiency of 99.8% in the cards detection task. The proposed method is a part of a project that aims to automate the process of broadcasting duplicate bridge competitions using a vision system and neural networks.

scholarly journals System Development for Liquid Chemicals Point Injection Based on Convolutional Neural Network Models

2021 ◽  
Vol 15 (2) ◽  
pp. 41-45
Author(s):  
V. S. Semenyuk ◽  
E. A. Nikitin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Plant Protection ◽  
System Development ◽  
Vision System ◽  
Network Models ◽  
Poor Quality ◽  
Neural Network Models ◽  
Validation Data ◽  
Point Injection

The authors showed that one of the reasons for the yield loss is poor-quality determination of the infection degree of agricultural crops by pathogens. They proposed a system of liquid chemicals point application. They identified the possibility of calculating the required amount of fertilizers and protective equipment. (Research purpose) To develop a system of liquid chemicals point application for plant protection and nutrition based on a convolutional neural network model. (Materials and methods) The authors analyzed the existing methods of machine learning. When developing the system, they used the U-net-algorithm of convolutional neural networks, as well as data displaying diseases of winter and spring wheat – brown rust and powdery mildew. Each image was cropped by hand and marked up using a specialized Python library. In the course of applying the architecture, the authors experimentally chose the optimal metrics (jaccard metric), the learning rate – 0.0001 seconds, the number of epochs – 300, and other indicators. (Results and discussion) The authors found that when a new, previously unavailable image was submitted to the algorithm, it recognized the disease in a few seconds and returned to the user not only the original image, but also a mask over it. The accuracy of applying the mask to the affected area was determined – 80 percent. They showed that the predicted error on the validation data was 0.18758. In practice, it could differ from the declared one by no more than 10-15 percent. The authors suggested using the algorithm with a vision system. (Conclusions) The authors showed that technical means imperfection for plants chemicalization increased the consumption up to 30 percent relative to the volume required for point application. They developed a neural network algorithm for identifying the affected areas of plants and proposed the concept of a point chemicals application in order to reduce the costs of processing crops. It was determined that the neural network was able to diagnose the affected areas of plants in 1 second.

Sign in / Sign up

Export Citation Format

Share Document