Integrating computer vision with yolov8 algorithm for PID: a state-of-the-art analysis

This research paper investigates the integration of the YOLOv8 algorithm with Proportional-Integral-Derivative (PID) control for real-time object detection and tracking. It explores improvements in the accuracy, efficiency, and real-time performance of the proposed YOLOv8-PID system, especially in scenarios requiring the differentiation of weapons and distinction between military and civilian personnel. Key performance factors are analyzed, and the system's potential for establishing new standards in computer vision, security, and autonomous systems is evaluated. Introduction The marriage of computer vision and real-time control systems has led to many innovations in various fields, but most notably, in the defence and security industries, where the ability to detect objects (such as threats) in real time and track them with accuracy is of the utmost importance. In these kind of fields, reliable detection systems need to recognize an object for what it is, differentiate between other objects, and react almost immediately. With the progression of detection algorithms, specifically the YOLO (You Only Look Once) series, real-time detection with high precision, which has a great effect on surveillance, self-navigation, and intelligent robotics, is possible. Object detection algorithms are great for stationary applications but many of them fail to perform in a mobile or dynamically changing environment, where it is imperative that focus be maintained on a moving object. This paper explores the use of YOLOv8 combined with PID control as an approach, with the goal of developing a system that does not only recognize objects with high accuracy, but also stabilizes tracking to account for real-time drifts and environmental changes. The idea is to improve the ability to detect weapons while distinguishing between civilians and military in changing situations, which could have large security implications, for example, autonomous drone surveillance. Objectives of this scientific paper are to:  Evaluate how effective the detection systems are, how well they detect small objects, and how fast they process.  Explore the use of YOLOv8 with PID control, to make tracking more stable and detection more accurate under adverse conditions.  Compare and contrast with current models, advantages and disadvantages.