Object Detection based on HSV in OpenCV

In the realm of computer vision and automation, the ability to identify and track objects in real time is critical for various applications, including robotics, surveillance, and machine control. This paper explores the process of controlling a Raspberry Pi computer equipped with a camera to detect and locate circular objects with a green color. By leveraging the power of open-source libraries and image processing techniques, the system performs real-time analysis of camera input to identify target objects based on color and shape. The research employs a literature review methodology, synthesizing information from relevant studies on computer vision, object recognition, and color filtering techniques. The paper focuses on the use of algorithms for color segmentation, contour detection, and shape recognition to achieve accurate object identification. Through this method, it examines the effectiveness and limitations of using a Raspberry Pi for object detection tasks in constrained environments. The findings indicate that Raspberry Pi, when coupled with optimized algorithms, provides a cost-effective solution for basic object detection tasks, though its performance is limited by processing power compared to more robust systems. This review emphasizes the significance of affordable solutions for educational and small-scale automation purposes, highlighting their potential impact on future applications in low-cost robotics and IoT devices.