Research of the Characteristics of a Convolutional Neural Network on the ESP32-CAM Microcontroller

Abstract

The paper is devoted to solving the problem of using neural networks for real-time image recognition on low-power portable devices running on microcontrollers. The ESP-32© CAM microcontroller was used as the target device, on which an artificial neural network was deployed, written using the Python® programming language and the Tensorflow© library for building neural networks. The performance of the microcontroller and personal computer for object detection using a neural network and their classification were compared in the paper. The image recognition time and percentage of correctly classified objects were compared. The paper shows that the number of training epochs affects the accuracy of object classification in the image. The obtained results show that increasing the number of training epochs increases the accuracy of object recognition using the studied neural network, but a significant increase in the number of epochs does not lead to a significant improvement in recognition accuracy. The difference in the obtained results for the microcontroller and personal computer image recognition accuracy ranges from 5%.