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Fast Recognition and Location Method of Parts for Assembly Robot Based on Deep Learning Network
Author(s) -
Yongwei Yu,
Jianheng Zhang,
Xin Han,
Liuqing Du,
LI Zheng-gen
Publication year - 2019
Publication title -
iop conference series materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/631/5/052010
Subject(s) - softmax function , artificial intelligence , computer science , pattern recognition (psychology) , robustness (evolution) , convolutional neural network , artificial neural network , convolution (computer science) , classifier (uml) , feature extraction , deep learning , robot , computer vision , biochemistry , chemistry , gene
Aiming at the problems of low components recognition rate and poor robustness under complex conditions such as adhesion, stacking and light source interference, a fast recognition and position method of parts based on deep convolution neural network was proposed. A multilayer convolution neural network for real-time detection of parts was constructed. Firstly, multi-scale extraction of candidate regions for target images was carried out. Then,the feature vectors of candidate candidate regions were automatically extracted by convolution neural network. The softmax classifier was used to recognize and locate. The experimental results show that the method realized the recognition and location for multiple parts in complex environment. The accuracy rate is over 95.2%.The detection took only 78ms.The accuracy rate and real-time performance of the method proved its feasibility.

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