REAL TIME OBJECT DETECTION USING OPENCV AND PYTHON

REAL TIME OBJECT DETECTION USING OPENCV AND PYTHON

M. Logeshwaran, R. Dhineshkumar, S. Siva, A. Rajamurugan

Mahendra Engineering College, Namakkal

Abstract

          This research addresses the burgeoning demand for efficient real-time object detection systems. The escalating need for accurate and swift identification of objects in diverse environments has motivated the exploration of advanced technologies. This paper focuses on leveraging OpenCV and Python to develop a real-time object detection system, employing machine learning techniques with the COCO dataset and the YOLO (You Only Look Once) algorithm. The problem statement revolves around the challenges associated with real-time object detection, including the need for rapid and accurate identification of objects across various domains. The proposed method integrates machine learning algorithms trained on the COCO dataset, enhancing the system's ability to recognize a broad spectrum of objects. YOLO is employed for its efficiency in simultaneously predicting multiple bounding boxes in real-time. Existing methods often face issues related to accuracy, speed, or adaptability to diverse scenarios. The utilization of the COCO dataset and YOLO algorithm in this proposed system aims to overcome these limitations. The results demonstrate a significant improvement in real-time object detection, showcasing the effectiveness of the proposed approach in terms of speed and accuracy across a wide range of object categories.

Keywords: Advanced Technologies, Identification, Object detection, Machine Learning