Title: Image recognition technology based on neural network in robot vision system

Authors: Yinggang He

Addresses: Chengyi University College, Jimei University, Xiamen 363000, Fujian, China

Abstract: This research uses CamVid training decoder to train the model, then fine tune the parameters on the collected data, label the manually collected data with LabetMe annotation tool, and cross verify the image and scene with neural network algorithm and image recognition principle technology. After five training cycles, the neural network in this study can achieve more than 90% recognition accuracy, and achieve convergence after storing about 10 cycles. Finally, the recognition accuracy in the test data set can reach more than 95%. In the range of robot vision recognition, the maximum measurement deviation is only 2.54 cm and the error is less than 2%. It can be concluded that the method used in this study has fast convergence speed, high recognition accuracy, small error, and good practicability and effectiveness.

Keywords: neural network; image recognition; machine vision; recognition system.

DOI: 10.1504/IJGUC.2021.119557

International Journal of Grid and Utility Computing, 2021 Vol.12 No.4, pp.415 - 424

Received: 06 Aug 2020
Accepted: 14 Sep 2020
Published online: 09 Dec 2021 *

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