Research on Pedestrian Target Intelligent Recognition Method Based on Neural Networks and Genetic Algorithms

2021 ◽  
Author(s):  
Aili Wang ◽  
Yuanyun Sun ◽  
Lu Li
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Recognition Method ◽  
Intelligent Recognition

Intelligent Recognition Method for Cigarette Code Based on Deep Neural Networks

2019 ◽  
Vol 31 (1) ◽  
pp. 111
Author(s):  
Zhifeng Xie ◽  
Jiaping Wu ◽  
Shuhan Zhang ◽  
Zhen Tang ◽  
Jie Fan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Recognition Method ◽  
Intelligent Recognition

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

scholarly journals Intelligent Recognition Method of Low-Altitude Squint Optical Ship Target Fused with Simulation Samples

2021 ◽  
Vol 13 (14) ◽  
pp. 2697
Author(s):  
Bo Liu ◽  
Qi Xiao ◽  
Yuhao Zhang ◽  
Wei Ni ◽  
Zhen Yang ◽  
...  
Keyword(s):  
Feature Recognition ◽  
Spectral Characteristics ◽  
Transmission Model ◽  
Sample Type ◽  
Recognition Method ◽  
Atmospheric Transmission ◽  
K Nearest Neighbors ◽  
Imaging Simulation ◽  
Low Altitude ◽  
Intelligent Recognition

To address the problem of intelligent recognition of optical ship targets under low-altitude squint detection, we propose an intelligent recognition method based on simulation samples. This method comprehensively considers geometric and spectral characteristics of ship targets and ocean background and performs full link modeling combined with the squint detection atmospheric transmission model. It also generates and expands squint multi-angle imaging simulation samples of ship targets in the visible light band using the expanded sample type to perform feature analysis and modification on SqueezeNet. Shallow and deeper features are combined to improve the accuracy of feature recognition. The experimental results demonstrate that using simulation samples to expand the training set can improve the performance of the traditional k-nearest neighbors algorithm and modified SqueezeNet. For the classification of specific ship target types, a mixed-scene dataset expanded with simulation samples was used for training. The classification accuracy of the modified SqueezeNet was 91.85%. These results verify the effectiveness of the proposed method.

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