scholarly journals Kenaf plant pest and disease detection using faster regional based convolutional neural network

2021 ◽  
Vol 24 (1) ◽  
pp. 198
Author(s):  
Alfita Rakhmandasari ◽  
Wayan Firdaus Mahmudy ◽  
Titiek Yulianti
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crop Production ◽  
Computation Time ◽  
Raw Material ◽  
Detection Accuracy ◽  
Pulp Fiber ◽  
Pests And Diseases ◽  
Processing Procedure ◽  
Selection Of

<span>Kenaf plant is a fibre plant whose stem bark is taken to be used as raw material for making geo-textile, particleboard, pulp, fiber drain, fiber board, and paper. The presence of plant pests and diseases that attack causes crop production to decrease. The detection of pests and diseases by farmers may be a challenging task. The detection can be done using artificial intelligence-based method. Convolutional neural networks (CNNs) are one of the most popular neural network architectures and have been successfully implemented for image classification. However, the CNN method is still considered a long time in the process, so this method was developed into namely faster regional based convolution neural network (RCNN). As the selection of the input features largely determines the accuracy of the results, a pre-processing procedure is developed to transform the kenaf plant image into input features of faster RCNN. A computational experiment proves that the faster RCNN has a very short computation time by completing 10000 iterations in 3 hours compared to convolutional neural network (CNN) completing 100 iterations at the same time. Furthermore, Faster RCNN gets 77.50% detection accuracy and bounding box accuracy 96.74% while CNN gets 72.96% detection accuracy at 400 epochs. The results also prove that the selection of input features and its pre-processing procedure could produce a high accuracy of detection. </span>

scholarly journals Methods for Preventing Visual Attacks in Convolutional Neural Networks Based on Data Discard and Dimensionality Reduction

2021 ◽  
Vol 11 (11) ◽  
pp. 5235
Author(s):  
Nikita Andriyanov
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Inference ◽  
Recognition Accuracy ◽  
Network Architectures ◽  
Reduced Dimensions ◽  
Different Types ◽  
Rectangular Area ◽  
Noise Impulse ◽  
Selection Of

The article is devoted to the study of convolutional neural network inference in the task of image processing under the influence of visual attacks. Attacks of four different types were considered: simple, involving the addition of white Gaussian noise, impulse action on one pixel of an image, and attacks that change brightness values within a rectangular area. MNIST and Kaggle dogs vs. cats datasets were chosen. Recognition characteristics were obtained for the accuracy, depending on the number of images subjected to attacks and the types of attacks used in the training. The study was based on well-known convolutional neural network architectures used in pattern recognition tasks, such as VGG-16 and Inception_v3. The dependencies of the recognition accuracy on the parameters of visual attacks were obtained. Original methods were proposed to prevent visual attacks. Such methods are based on the selection of “incomprehensible” classes for the recognizer, and their subsequent correction based on neural network inference with reduced image sizes. As a result of applying these methods, gains in the accuracy metric by a factor of 1.3 were obtained after iteration by discarding incomprehensible images, and reducing the amount of uncertainty by 4–5% after iteration by applying the integration of the results of image analyses in reduced dimensions.

scholarly journals Fpga Implementation of Precise Convolutional Neural Network for Extreme Learning Machine

2020 ◽  
Vol 9 (8) ◽  
pp. 470-480
Keyword(s):  
Neural Network ◽  
Receptive Field ◽  
Convolutional Neural Network ◽  
Computation Time ◽  
Field Approach ◽  
Pixel Array ◽  
Gradient Based ◽  
Feed Forward Neural Networks ◽  
Learning Machine ◽  
Hidden Layer

Feed-forward neural networks can be trained based on a gradient-descent based backpropagation algorithm. But, these algorithms require more computation time. Extreme Learning Machines (ELM’s) are time-efficient, and they are less complicated than the conventional gradient-based algorithm. In previous years, an SRAM based convolutional neural network using a receptive – field Approach was proposed. This neural network was used as an encoder for the ELM algorithm and was implemented on FPGA. But, this neural network used an inaccurate 3-stage pipelined parallel adder. Hence, this neural network generates imprecise stimuli to the hidden layer neurons. This paper presents an implementation of precise convolutional neural network for encoding in the ELM algorithm based on the receptive - field approach at the hardware level. In the third stage of the pipelined parallel adder, instead of approximating the output by using one 2-input 15-bit adder, one 4-input 14-bit adder is used. Also, an additional weighted pixel array block is used. This weighted pixel array improves the accuracy of generating 128 weighted pixels. This neural network was simulated using ModelSim-Altera 10.1d and synthesized using Quartus II 13.0 sp1. This neural network is implemented on Cyclone V FPGA and used for pattern recognition applications. Although this design consumes slightly more hardware resources, this design is more accurate compared to previously existing encoders

scholarly journals RICNN: A ResNet&Inception convolutional neural network for intrusion detection of abnormal traffic

2021 ◽  
pp. 55-55
Author(s):  
Benhui Xia ◽  
Dezhi Han ◽  
Ximing Yin ◽  
Gao Na
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Detection Rate ◽  
Recognition Rate ◽  
Identification Accuracy ◽  
Detection Accuracy ◽  
Traffic Classification ◽  
High Detection Rate ◽  
Network Intrusion

To secure cloud computing and outsourced data while meeting the requirements of automation, many intrusion detection schemes based on deep learn ing are proposed. Though the detection rate of many network intrusion detection solutions can be quite high nowadays, their identification accuracy on imbalanced abnormal network traffic still remains low. Therefore, this paper proposes a ResNet &Inception-based convolutional neural network (RICNN) model to abnormal traffic classification. RICNN can learn more traffic features through the Inception unit, and the degradation problem of the network is eliminated through the direct map ping unit of ResNet, thus the improvement of the model?s generalization ability can be achievable. In addition, to simplify the network, an improved version of RICNN, which makes it possible to reduce the number of parameters that need to be learnt without degrading identification accuracy, is also proposed in this paper. The experimental results on the dataset CICIDS2017 show that RICNN not only achieves an overall accuracy of 99.386% but also has a high detection rate across different categories, especially for small samples. The comparison experiments show that the recognition rate of RICNN outperforms a variety of CNN models and RNN models, and the best detection accuracy can be achieved.

scholarly journals A Single Target Grasp Detection Network Based on Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Longzhi Zhang ◽  
Dongmei Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
High Accuracy ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Single Object ◽  
Single Target ◽  
High Detection ◽  
Good Detection

Grasp detection based on convolutional neural network has gained some achievements. However, overfitting of multilayer convolutional neural network still exists and leads to poor detection precision. To acquire high detection accuracy, a single target grasp detection network that generalizes the fitting of angle and position, based on the convolution neural network, is put forward here. The proposed network regards the image as input and grasping parameters including angle and position as output, with the detection manner of end-to-end. Particularly, preprocessing dataset is to achieve the full coverage to input of model and transfer learning is to avoid overfitting of network. Importantly, a series of experimental results indicate that, for single object grasping, our network has good detection results and high accuracy, which proves that the proposed network has strong generalization in direction and category.

scholarly journals DRPnet - Automated Particle Picking in Cryo-Electron Micrographs using Deep Regression

2019 ◽  
Author(s):  
Nguyen P. Nguyen ◽  
Jacob Gotberg ◽  
Ilker Ersoy ◽  
Filiz Bunyak ◽  
Tommi White
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Electron Micrographs ◽  
Signal To Noise Ratio ◽  
Particle Analysis ◽  
User Interactions ◽  
Distance Map ◽  
Protein Particles ◽  
Selection Of ◽  
Specific Particle

AbstractSelection of individual protein particles in cryo-electron micrographs is an important step in single particle analysis. In this study, we developed a deep learning-based method to automatically detect particle centers from cryoEM micrographs. This is a challenging task because of the low signal-to-noise ratio of cryoEM micrographs and the size, shape, and grayscale-level variations in particles. We propose a double convolutional neural network (CNN) cascade for automated detection of particles in cryo-electron micrographs. Particles are detected by the first network, a fully convolutional regression network (FCRN), which maps the particle image to a continuous distance map that acts like a probability density function of particle centers. Particles identified by FCRN are further refined (or classified) to reduce false particle detections by the second CNN. This approach, entitled Deep Regression Picker Network or “DRPnet”, is simple but very effective in recognizing different grayscale patterns corresponding to 2D views of 3D particles. Our experiments showed that DRPnet’s first CNN pretrained with one dataset can be used to detect particles from a different datasets without retraining. The performance of this network can be further improved by re-training the network using specific particle datasets. The second network, a classification convolutional neural network, is used to refine detection results by identifying false detections. The proposed fully automated “deep regression” system, DRPnet, pretrained with TRPV1 (EMPIAR-10005) [1], and tested on β-galactosidase (EMPIAR-10017) [2] and β-galactosidase (EMPIAR-10061) [3], was then compared to RELION’s interactive particle picking. Preliminary experiments resulted in comparable or better particle picking performance with drastically reduced user interactions and improved processing time.

scholarly journals Convolutional Neural Network Using Kalman Filter for Human Detection and Tracking on RGB-D Video

2018 ◽  
Vol 12 (2) ◽  
pp. 105 ◽  
Author(s):  
Jovin Angelico ◽  
Ken Ratri Retno Wardani
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Convolutional Neural Network ◽  
Human Detection ◽  
Detection Accuracy ◽  
Tracking Accuracy ◽  
Lighting Condition ◽  
Detection And Tracking ◽  
Computation Efficiency ◽  
The Impact

The computer ability to detect human being by computer vision is still being improved both in accuracy or computation time. In low-lighting condition, the detection accuracy is usually low. This research uses additional information, besides RGB channels, namely a depth map that shows objects’ distance relative to the camera. This research integrates Cascade Classifier (CC) to localize the potential object, the Convolutional Neural Network (CNN) technique to identify the human and nonhuman image, and the Kalman filter technique to track human movement. For training and testing purposes, there are two kinds of RGB-D datasets used with different points of view and lighting conditions. Both datasets have been selected to remove images which contain a lot of noises and occlusions so that during the training process it will be more directed. Using these integrated techniques, detection and tracking accuracy reach 77.7%. The impact of using Kalman filter increases computation efficiency by 41%.

scholarly journals Light Intensity Adjustment and Severity Estimation for the Generic Disease of Tomato Plants

2021 ◽  
pp. 353-358
Author(s):  
Faizan Ahmed Sayyad ◽  
Rehan Ahmed Sayyad
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Crop Production ◽  
Disease Classification ◽  
Attention Networks ◽  
Severity Estimation ◽  
Image Capturing ◽  
Severity Of The Disease ◽  
Key Steps

Many research has been done on detecting the type of disease that affects the crops. Because of this the farmers use pesticides to reduce the loss of crop production, since they don’t know how much pesticides to spray or use, they tend to overuse them which eventually leads to further destruction of crops. For disease classification Convolutional Neural Network (CNN) is being used which lets you know what kind of disease has affected the crop. In this paper we have worked on self attention networks to calculate the severity of the disease on the leaf . Self Attention Network introduced in the architecture lets the model learn the feature more efficiently and focus more on the affected region of the leaf. The model was trained and tested on the standard dataset (Plant Village) . The core processes comprises image capturing, image processing and testing on Self Attention Convolutional Neural Network architecture.. All of the key steps required to implement the model are detailed throughout the document.

Outlier Detection Using Convolutional Neural Network for Wireless Sensor Network

2021 ◽  
Vol 17 (2) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Wireless Sensor Network ◽  
Convolutional Neural Network ◽  
Sensor Network ◽  
Time Series Data ◽  
Wireless Sensor ◽  
Series Data ◽  
Detection Accuracy

Over the recent years, the term deep learning has been considered as one of the primary choice for handling huge amount of data. Having deeper hidden layers, it surpasses classical methods for detection of outlier in wireless sensor network. The Convolutional Neural Network (CNN) is a biologically inspired computational model which is one of the most popular deep learning approaches. It comprises neurons that self-optimize through learning. EEG generally known as Electroencephalography is a tool used for investigation of brain function and EEG signal gives time-series data as output. In this paper, we propose a state-of-the-art technique designed by processing the time-series data generated by the sensor nodes stored in a large dataset into discrete one-second frames and these frames are projected onto a 2D map images. A convolutional neural network (CNN) is then trained to classify these frames. The result improves detection accuracy and encouraging.

scholarly journals Attention-mechanism-based tracking method for intelligent Internet of vehicles

2018 ◽  
Vol 14 (10) ◽  
pp. 155014771880594 ◽  
Author(s):  
Xu Kang ◽  
Bin Song ◽  
Jie Guo ◽  
Xiaojiang Du ◽  
Mohsen Guizani
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Sensor ◽  
Attention Mechanism ◽  
Vehicle Tracking ◽  
Background Region ◽  
Internet Of Vehicles ◽  
Tracking Method ◽  
Siamese Network ◽  
Selection Of

Vehicle tracking task plays an important role on the Internet of vehicles and intelligent transportation system. Beyond the traditional Global Positioning System sensor, the image sensor can capture different kinds of vehicles, analyze their driving situation, and can interact with them. Aiming at the problem that the traditional convolutional neural network is vulnerable to background interference, this article proposes vehicle tracking method based on human attention mechanism for self-selection of deep features with an inter-channel fully connected layer. It mainly includes the following contents: (1) a fully convolutional neural network fused attention mechanism with the selection of the deep features for convolution; (2) a separation method for template and semantic background region to separate target vehicles from the background in the initial frame adaptively; (3) a two-stage method for model training using our traffic dataset. The experimental results show that the proposed method improves the tracking accuracy without an increase in tracking time. Meanwhile, it strengthens the robustness of algorithm under the condition of the complex background region. The success rate of the proposed method in overall traffic datasets is higher than Siamese network by about 10%, and the overall precision is higher than Siamese network by 8%.

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