Design of an embedded machine vision system for smart cameras

Abstract

Abstract With the rapid increase in computer users’ requirements for image information and image processing, and the rapid development of the intelligent process, the ability of the traditional visual system to process image information and data has been difficult to meet the needs of users. Therefore, in this article, we upgrade the vision system of smart cameras by introducing three network algorithm structures: convolutional neural network (CNN), LSTM and CNN-LSTM. We compare the 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%, the precision can reach 87.5% and the recall rate is 99.8%; the LSTM accuracy is 97.7%, its precision is 89.6% and its recall rate is 87.3%; its precision can be improved to 90.5% and the recall rate to 99.7%.