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 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.

A seismic fault recognition method based on ant colony optimization

2018 ◽  
Vol 152 ◽  
pp. 1-8 ◽  
Author(s):  
Lei Chen ◽  
Chuangbai Xiao ◽  
Xueliang Li ◽  
Zhenli Wang ◽  
Shoudong Huo
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Recognition Method ◽  
Seismic Fault ◽  
Fault Recognition
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