A Novel Target Recognition System for the Amphibious Robot based on Edge Computing and Neural Network

2020 ◽  
Author(s):  
Shuxiang Guo ◽  
Handong Cheng ◽  
Jian Guo ◽  
Jigang Xu
Keyword(s):  
Neural Network ◽  
Target Recognition ◽  
Recognition System ◽  
Edge Computing ◽  
Novel Target ◽  
Amphibious Robot

Artificial Neural Network Based Target Recognition for Marine Search

2017 ◽  
Vol 8 (3) ◽  
pp. 616
Author(s):  
Capt. V. Ramachandran
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Target Recognition ◽  
Recognition System ◽  
Search Method ◽  
Search And Rescue ◽  
Radar Images ◽  
Invariant Moments ◽  
Artificial Neural ◽  
Detection And Recognition

<p>The key point of marine search and rescue is to find out and recognize the distress objects. At present, the visual search method is usually adopted to detect the ships in distress, and this method can only be used at good sea condition and visibility. In this paper, a new target detection and recognition system is proposed. The parameters of radar transmitter and echo graphics and the invariant moments of radar images are extracted as the system’s recognition features, and the system’s target classifier is based on Artificial Neural Networks (ANN). The developed recognition classifier has been tested using three kinds of target Images, the target’s features are used as the inputs of trained ANN and the outputs of networks are target classification. Sea experimental results show that the proposed method is well-clustering and with high classified accuracy.</p>

Convolutional Neural Network Based Target Recognition for Marine Search

2017 ◽  
Vol 8 (2) ◽  
pp. 561 ◽  
Author(s):  
J. S. Ashwin ◽  
N. Manoharan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Visual Search ◽  
Target Recognition ◽  
Recognition System ◽  
Search Method ◽  
Search And Rescue ◽  
Radar Images ◽  
Invariant Moments ◽  
Detection And Recognition

<p>The key point of marine search and rescue is to find out and recognize the distress objects. At present, the visual search method is usually adopted to detect the ships in distress, and this method can only be used at good sea condition and visibility. In this paper, a new target detection and recognition system is proposed. The parameters of radar transmitter and echo graphics and the invariant moments of radar images are extracted as the system’s recognition features, and the system’s target classifier is based on Convolutional Neural Networks (CNN). The developed recognition classifier has been tested using three kinds of target Images, the target’s features are used as the inputs of trained CNN and the outputs of networks are target classification. Sea experimental results show that the proposed method is well-clustering and with high classified accuracy.</p>

scholarly journals Target Recognition of Industrial Robots Using Machine Vision in 5G Environment

2021 ◽  
Vol 15 ◽  
Author(s):  
Zhenkun Jin ◽  
Lei Liu ◽  
Dafeng Gong ◽  
Lei Li
Keyword(s):  
Neural Network ◽  
Visual Recognition ◽  
Simulation Analysis ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Industrial Robot ◽  
Recognition System ◽  
Industrial Robots ◽  
Network Algorithms ◽  
Positioning Errors

The purpose is to solve the problems of large positioning errors, low recognition speed, and low object recognition accuracy in industrial robot detection in a 5G environment. The convolutional neural network (CNN) model in the deep learning (DL) algorithm is adopted for image convolution, pooling, and target classification, optimizing the industrial robot visual recognition system in the improved method. With the bottled objects as the targets, the improved Fast-RCNN target detection model's algorithm is verified; with the small-size bottled objects in a complex environment as the targets, the improved VGG-16 classification network on the Hyper-Column scheme is verified. Finally, the algorithm constructed by the simulation analysis is compared with other advanced CNN algorithms. The results show that both the Fast RCN algorithm and the improved VGG-16 classification network based on the Hyper-Column scheme can position and recognize the targets with a recognition accuracy rate of 82.34%, significantly better than other advanced neural network algorithms. Therefore, the improved VGG-16 classification network based on the Hyper-Column scheme has good accuracy and effectiveness for target recognition and positioning, providing an experimental reference for industrial robots' application and development.

Design of infrared target recognition system with optimized convolutional neural network

2021 ◽  
Vol 29 (4) ◽  
pp. 822-831
Author(s):  
Ke-jia LIU ◽  
◽  
Rong-sheng MA ◽  
Zi-mu TANG ◽  
Jie LIANG ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Recognition ◽  
Recognition System

Study on Rotary Object Recognition Technique from the Complex Background

2011 ◽  
Vol 418-420 ◽  
pp. 494-500
Author(s):  
Bao Zhang Li ◽  
Mo Yu Sha ◽  
Yan Ping Cui
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Bp Neural Network ◽  
Target Recognition ◽  
Median Filter ◽  
Recognition System ◽  
Invariant Moments ◽  
Complex Background ◽  
Recognition Result ◽  
The Military

Target recognition from complex background is the emphasis and difficulty of computer vision, and rotary objects is widely used in the military and manufacturing field. Rotary object recognition in complex background based on improved BP neural network is proposed in the dissertation. Median filter is adopted to get rid of the noise and an improved method of maximum classes square error is used to compute the threshold of the image segmentation. The target recognition system based on improved BP neural network is established to recognize the rotary objects, and seven invariant moments of rotary objects serve as the input feature vector. The experiment results show that the image noise could be gotten rid of effectively and the image could be segmented exactly by the image preprocessing method put forward in the dissertation, and the seven invariant moments is appropriate for the character of rotary objects, and the rotary object recognition system based on BP neural network acquires an excellent recognition result.

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