scholarly journals Dual-channel convolutional neural network for power edge image recognition

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Fangrong Zhou ◽  
Yi Ma ◽  
Bo Wang ◽  
Gang Lin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Classification Method ◽  
Power Equipment ◽  
Recognition Method ◽  
Edge Image ◽  
Dual Channel

AbstractIn view of the low accuracy and poor processing capacity of traditional power equipment image recognition methods, this paper proposes a power equipment image recognition method based on a dual-channel convolutional neural network (DC-CNN) model and random forest (RF) classification. In the aspect of feature extraction, the DC-CNN model extracts the characteristics of power equipment through two independent CNN models. In the aspect of the recognition algorithm, by referring to the advantages of the traditional machine learning method and incorporating the advantages of the RF, an RF classification method incorporating deep learning is proposed. Finally, the proposed DC-CNN model and RF classification method are used to classify images of various types of power equipment. The results show that the proposed methods can be effectively applied to the image recognition of various types of power equipment, and they greatly improve the recognition rate of power equipment images.

CloudA: A Ground-Based Cloud Classification Method with a Convolutional Neural Network

2020 ◽  
Vol 37 (9) ◽  
pp. 1661-1668
Author(s):  
Min Wang ◽  
Shudao Zhou ◽  
Zhong Yang ◽  
Zhanhua Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Learning Ability ◽  
Visualization Method ◽  
Classification Methods ◽  
Recognition Method ◽  
Cloud Classification ◽  
Average Accuracy

AbstractConventional classification methods are based on artificial experience to extract features, and each link is independent, which is a kind of “shallow learning.” As a result, the scope of the cloud category applied by this method is limited. In this paper, we propose a new convolutional neural network (CNN) with deep learning ability, called CloudA, for the ground-based cloud image recognition method. We use the Singapore Whole-Sky Imaging Categories (SWIMCAT) sample library and total-sky sample library to train and test CloudA. In particular, we visualize the cloud features captured by CloudA using the TensorBoard visualization method, and these features can help us to understand the process of ground-based cloud classification. We compare this method with other commonly used methods to explore the feasibility of using CloudA to classify ground-based cloud images, and the evaluation of a large number of experiments show that the average accuracy of this method is nearly 98.63% for ground-based cloud classification.

scholarly journals Image Recognition Method Based on an Improved Convolutional Neural Network to Detect Impurities in Wheat

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 162206-162218 ◽  
Author(s):  
Yin Shen ◽  
Yanxin Yin ◽  
Chunjiang Zhao ◽  
Bin Li ◽  
Jun WANG ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Recognition Method

An Image Recognition Method Based on Ant Colony Optimization Neural Network Using in Imaging Fuze

2011 ◽  
Vol 121-126 ◽  
pp. 1886-1890
Author(s):  
Ke Yong Wang ◽  
Shi Kai Xing
Keyword(s):  
Neural Network ◽  
Ant Colony Optimization ◽  
Image Recognition ◽  
Recognition Algorithm ◽  
Bp Algorithm ◽  
Ant Colony ◽  
Target Image ◽  
Recognition Method ◽  
Simulation Experiments ◽  
Training Time

Target image recognition is an important issue in the information processing of imaging fuse system. In the paper, the main frame is proposed which can solve the problem of target image recognition and many computer simulation experiments are carried out. A recognition algorithm based on ant colony optimization and neural network is proposed. It overcomes the shortcomings of traditional BP algorithm and converges fast. The results of experiments prove that the presented algorithm can shorten the training time effectively and increase the accuracy of recognition, so it is very useful in improving the effective destroying ability of the missile.

scholarly journals An effective face recognition method using guided image filter and convolutional neural network

2021 ◽  
Vol 23 (3) ◽  
pp. 1699
Author(s):  
Yallamandaiah S. ◽  
Purnachand N.
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Recognition Method ◽  
Image Filter ◽  
Guided Image Filter ◽  
Face Region ◽  
The Face ◽  
Recognition Systems

<p>In the area of computer vision, face recognition is a challenging task because of the pose, facial expression, and illumination variations. The performance of face recognition systems reduces in an unconstrained environment. In this work, a new face recognition approach is proposed using a guided image filter, and a convolutional neural network (CNN). The guided image filter is a smoothing operator and performs well near the edges. Initially, the ViolaJones algorithm is used to detect the face region and then smoothened by a guided image filter. Later the proposed CNN is used to extract the features and recognize the faces. The experiments were performed on face databases like ORL, JAFFE, and YALE and attained a recognition rate of 98.33%, 99.53%, and 98.65% respectively. The experimental results show that the suggested face recognition method attains good results than some of the state-of-the-art techniques.</p>

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