Wireless Surveillance with Human Detection Using Artificial Intelligence and Drone

Abstract

Conventional human-operated surveillance is prone to errors caused by distractions, fatigue, or biases. The proposed project seeks to improve the effectiveness and precision of surveillance by utilizing an image detection algorithm and the Internet of Things (IoT). This project overcomes the limitations of human detection by deploying a drone equipped with a camera that is able to identify people and suspicious individuals. The system is designed to identify individuals within the drone's visual range, evaluate the accuracy of person detection, and provide immediate monitoring via IoT connectivity. Person detection is performed using the You Only Look Once (YOLO) algorithm, and the project's scope is limited by the camera's quality, field of view, and resolution, which restrict its ability to recognize individuals within a certain range. Based on the results from a dataset of 3,500 images, the person detection algorithm achieves a mean average precision (mAP) value of 0.835, a confidence ratio of 0.79, and high accuracy in the confusion matrix. Performance is further improved by integrating OpenVINO for computers running on Intel CPUs. The IoT-based monitoring system is implemented using a Streamlit web-based application and Telegram application. The results show a 2-second delay in streaming from the Raspberry Pi to the host computer, with an average speed of 100 ms per frame.