Design and implementation monitoring robotic system based on you only look once model using deep learning technique

Abstract

The need for robotics systems has become an urgent necessity in various fields, especially in video surveillance and live broadcasting systems. This work is aimed to design and implement a robotic system which is based mainly on raspberry pi 4 model B to control this overall system and display a live video by using a webcam (USB camera) as well as using (YOLOv5) you only look once algorithm-version five a deep learning-based object detector to detect, recognize and display objects in real-time. This deep learning algorithm is highly accurate and fast and is implemented by Python, OpenCV, PyTorch codes and the Context Object Detection Task (COCO) 2020 dataset. This robot can move in all directions and in different places especially in undesirable places to transmit live video with a moving camera and process it by the YOLOv5 model. Also, the robot system can receive images, videos, or YouTube links and process them with YOLOv5. Raspberry Pi is controlled remotely by connecting to the network through Wi-Fi locally or publicly using the internet with a remote desktop connection application. The results were very satisfactory and proved the high-performance efficiency of the robot.