Open AccessA Real-time Image Recognition System Based on Improved Jacintonet Convolutional Neural Network
Author(s) -
Shixing Chen,
Hongfang Yuan,
Xi Cao,
Xiang Li
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1576/1/012004
Subject(s) - computer science , convolutional neural network , embedded system , automation , network architecture , real time computing , artificial neural network , set (abstract data type) , core (optical fiber) , computer architecture , artificial intelligence , computer network , engineering , telecommunications , mechanical engineering , programming language
With the emergence and development of new automation industries such as unattended supermarkets and smart picking orchards, the demand for real-time systems based on embedded platforms is increasing day by day. Heterogeneous multi-core processors are widely used in modern integrated circuit design due to their advantages of low power consumption and high parallelism. More and more real-time systems are implemented on heterogeneous multi-core platforms. Based on the heterogeneous multi-core embedded system, the network parameters and architecture of jacintonet model are improved, and a real-time system for fruit image recognition is realized by using the improved network. A small sample data set is used to train the modified network and the trained network is imported into the system for testing. The result shows that the improved jacintonet network can run well on heterogeneous multi-core system and has the same recognition performance as the original network.