Resumen de Design of an embedded machine vision system for smart cameras

Zhongxiang Zhou, Wentao Liu, Kewei Cai, Haojie Lu, Yao Fu

• With the rapid increase in computer users’ requirements for image information and image processing, and the rapid de-velopment of the intelligent process, the ability of the traditional visual system to process image information and data hasbeen difficult to meet the needs of users. Therefore, in this article, we upgrade the vision system of smart cameras by in-troducing three network algorithm structures: convolutional neural network (CNN), LSTM and CNN-LSTM. We comparethe classification performance of the three algorithms and evaluate them with three metrics: accuracy, precision and recall.The experimental results show that using the CNN algorithm, the accuracy of image information processing is 98.2%, theprecision can reach 87.5% and the recall rate is 99.8%; the LSTM accuracy is 97.7%, its precision is 89.6% and its recallrate is 87.3%; its precision can be improved to 90.5% and the recall rate to 99.7%