Precise Selection and Visualization of Maize Kernels Based on Electromagnetic Vibration and Deep Learning

2020 ◽  
Vol 63 (3) ◽  
pp. 629-643
Author(s):  
Chengshun Zhao ◽  
Longzhe Quan ◽  
Hailong Li ◽  
Ruiqi Liu ◽  
Jianyu Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Computer Hardware ◽  
Learning Technology ◽  
Average Precision ◽  
Maize Kernel ◽  
Kernel Selection ◽  
Maize Kernels ◽  
Selection Of

Abstract. With the development of precision agriculture, the selection of maize kernels has gained more importance in scientific research and practical significance in agricultural production. In this study, the deep learning technology of machine vision was used to select maize kernels, solving the problems of previous maize kernel selection for specific sorting problems, the cumbersome process of artificial feature modeling, the problem of a small number of features, and the challenge of limited data. First, the maximum size of a model based on convolutional neural networks (CNNs) that could run under finite hardware conditions was determined by experiments. Four different network models (Faster R-CNN, Model 1.0, Model 2.0, and Model 3.0) were then designed and trained using a data set of maize kernels. Finally, the accuracy of the models was verified by comparison test, and the detection results of the models were analyzed according to their precision, recall, FPR, F1, precision-recall curve, average precision (AP), mean average precision (mAP), and detection speed. The results show that for the validation set not used for training, Model 1.0 had the highest average recall rate of 98.42% among the four models. Without taking into account the identification of the removed kernels, only excellent maize kernels were identified, and the mAP of Model 1.0 was as high as 97.27%. Moreover, Model 1.0 requires less computer resources, and its computer hardware requirement is lower. The precision, recall, and F1 value of Model 2.0 were increased by 3.73%, 3.55%, and 3.79%, respectively, and the false positive rate of Model 2.0 was reduced by 1.31% on average compared with the Faster R-CNN model. By comparing Model 1.0, Model 2.0, and Model 3.0, it was found that the overall performance of Model 2.0 was best. The size of the network model has an effect on the accurate selection of maize kernels, and a moderate-size model is the best. This study laid a good foundation for the further application of deep learning technology in the real-time sorting of maize kernels and additional applications in the field of agriculture. Keywords: Convolutional neural networks, Deep learning, Maize kernel, Selection, Visualization.

scholarly journals Image recognition algorithms based on deep learning

2021 ◽  
Vol 2137 (1) ◽  
pp. 012056
Author(s):  
Hongli Ma ◽  
Fang Xie ◽  
Tao Chen ◽  
Lei Liang ◽  
Jie Lu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Development Process ◽  
Research Direction ◽  
Learning Technology ◽  
Convolutional Network ◽  
Important Research Direction

Abstract Convolutional neural network is a very important research direction in deep learning technology. According to the current development of convolutional network, in this paper, convolutional neural networks are induced. Firstly, this paper induces the development process of convolutional neural network; then it introduces the structure of convolutional neural network and some typical convolutional neural networks. Finally, several examples of the application of deep learning is introduced.

scholarly journals Experimental Discussion on Fire Image Recognition Based on Deep Learning

2021 ◽  
Vol 2066 (1) ◽  
pp. 012071
Author(s):  
Yongyi Cui ◽  
Fang Qu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Rapid Development ◽  
Fire Detection ◽  
Learning Technology ◽  
Fire Recognition

Abstract Fire detection technology based on video images is an emerging technology that has its own unique advantages in many aspects. With the rapid development of deep learning technology, Convolutional Neural Networks based on deep learning theory show unique advantages in many image recognition fields. This paper uses Convolutional Neural Networks to try to identify fire in video surveillance images. This paper introduces the main processing flow of Convolutional Neural Networks when completing image recognition tasks, and elaborates the basic principles and ideas of each stage of image recognition in detail. The Pytorch deep learning framework is used to build a Convolutional Neural Network for training, verification and testing for fire recognition. In view of the lack of a standard and authoritative fire recognition training set, we have conducted experiments on fires with various interference sources under various environmental conditions using a variety of fuels in the laboratory, and recorded videos. Finally, the Convolutional Neural Network was trained, verified and tested by using experimental videos, fire videos on the Internet as well as other interference source videos that may be misjudged as fires.

DEEP LEARNING TECHNOLOGY OF CONVOLUTIONAL NEURAL NETWORKS FOR FACIAL EXPRESSION RECOGNITION

2021 ◽  
Vol 4 (2) ◽  
pp. 192-201
Author(s):  
Denys Valeriiovych Petrosiuk ◽  
Olena Oleksandrivna Arsirii ◽  
Oksana Yurievna Babilunha ◽  
Anatolii Oleksandrovych Nikolenko
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Facial Expression ◽  
Facial Expressions ◽  
Convolutional Neural Networks ◽  
Facial Expression Recognition ◽  
Motor Units ◽  
Learning Technology ◽  
Expression Recognition

The application of deep learning convolutional neural networks for solving the problem of automated facial expression recognition and determination of emotions of a person is analyzed. It is proposed to use the advantages of the transfer approach to deep learning convolutional neural networks training to solve the problem of insufficient data volume in sets of images with different facial expressions. Most of these datasets are labeled in accordance with a facial coding system based on the units of human facial movement. The developed technology of transfer learning of the public deep learning convolutional neural networks families DenseNet and MobileNet, with the subsequent “fine tuning” of the network parameters, allowed to reduce the training time and computational resources when solving the problem of facial expression recognition without losing the reliability of recognition of motor units. During the development of deep learning technology for convolutional neural networks, the following tasks were solved. Firstly, the choice of publicly available convolutional neural networks of the DenseNet and MobileNet families pre-trained on the ImageNet dataset was substantiated, taking into account the peculiarities of transfer learning for the task of recognizing facial expressions and determining emotions. Secondary, a model of a deep convolutional neural network and a method for its training have been developed for solving problems of recognizing facial expressions and determining human emotions, taking into account the specifics of the selected pretrained convolutional neural networks. Thirdly, the developed deep learning technology was tested, and finally, the resource intensity and reliability of recognition of motor units on the DISFA set were assessed. The proposed technology of deep learning of convolutional neural networks can be used in the development of systems for automatic recognition of facial expressions and determination of human emotions for both stationary and mobile devices. Further modification of the systems for recognizing motor units of human facial activity in order to increase the reliability of recognition is possible using of the augmentation technique.

scholarly journals Deep convolutional neural networks for cardiovascular vulnerable plaque detection

2019 ◽  
Vol 277 ◽  
pp. 02024 ◽  
Author(s):  
Lincan Li ◽  
Tong Jia ◽  
Tianqi Meng ◽  
Yizhe Liu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Vulnerable Plaque ◽  
Recall Rate ◽  
Superior Performance ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Vulnerable Plaques ◽  
Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

scholarly journals Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1579
Author(s):  
Dongqi Wang ◽  
Qinghua Meng ◽  
Dongming Chen ◽  
Hupo Zhang ◽  
Lisheng Xu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Channel Model ◽  
Expert Knowledge ◽  
Automatic Detection ◽  
Data Representation ◽  
Learning Technology ◽  
Arrhythmia Detection ◽  
Automatic Feature Extraction

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

scholarly journals Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images

2021 ◽  
Vol 11 (5) ◽  
pp. 2284
Author(s):  
Asma Maqsood ◽  
Muhammad Shahid Farid ◽  
Muhammad Hassan Khan ◽  
Marcin Grzegorzek
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Red Blood Cells ◽  
Convolutional Neural Networks ◽  
Blood Cells ◽  
Superior Performance ◽  
Learning Models ◽  
Infected Female ◽  
The World ◽  
Augmentation Techniques

Malaria is a disease activated by a type of microscopic parasite transmitted from infected female mosquito bites to humans. Malaria is a fatal disease that is endemic in many regions of the world. Quick diagnosis of this disease will be very valuable for patients, as traditional methods require tedious work for its detection. Recently, some automated methods have been proposed that exploit hand-crafted feature extraction techniques however, their accuracies are not reliable. Deep learning approaches modernize the world with their superior performance. Convolutional Neural Networks (CNN) are vastly scalable for image classification tasks that extract features through hidden layers of the model without any handcrafting. The detection of malaria-infected red blood cells from segmented microscopic blood images using convolutional neural networks can assist in quick diagnosis, and this will be useful for regions with fewer healthcare experts. The contributions of this paper are two-fold. First, we evaluate the performance of different existing deep learning models for efficient malaria detection. Second, we propose a customized CNN model that outperforms all observed deep learning models. It exploits the bilateral filtering and image augmentation techniques for highlighting features of red blood cells before training the model. Due to image augmentation techniques, the customized CNN model is generalized and avoids over-fitting. All experimental evaluations are performed on the benchmark NIH Malaria Dataset, and the results reveal that the proposed algorithm is 96.82% accurate in detecting malaria from the microscopic blood smears.

Efficient downscaling of satellite oceanographic data with convolutional neural networks

2021 ◽  
Vol 12 (3) ◽  
pp. 46-47
Author(s):  
Nikita Saxena
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Sea Surface ◽  
Clear Sky ◽  
Oceanographic Data ◽  
Computationally Expensive ◽  
Sky Conditions ◽  
Data Inference

Space-borne satellite radiometers measure Sea Surface Temperature (SST), which is pivotal to studies of air-sea interactions and ocean features. Under clear sky conditions, high resolution measurements are obtainable. But under cloudy conditions, data analysis is constrained to the available low resolution measurements. We assess the efficiency of Deep Learning (DL) architectures, particularly Convolutional Neural Networks (CNN) to downscale oceanographic data from low spatial resolution (SR) to high SR. With a focus on SST Fields of Bay of Bengal, this study proves that Very Deep Super Resolution CNN can successfully reconstruct SST observations from 15 km SR to 5km SR, and 5km SR to 1km SR. This outcome calls attention to the significance of DL models explicitly trained for the reconstruction of high SR SST fields by using low SR data. Inference on DL models can act as a substitute to the existing computationally expensive downscaling technique: Dynamical Downsampling. The complete code is available on this Github Repository.

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