Enhancing Real-Time Object Detection With Advanced YOLOv9 and OpenCV in Python

Object detection is a foundation process in computer vision having widespread applications in autonomous driving, medical diagnostics and security monitoring. Recent advancements and development in field of machine learning, particularly deep learning had dynamically improved object detection accuracy. However, the selection of an optimal algorithm remains challenging due to the diverse characteristics of available techniques. This paper depicts the comprehensive review of object detection, covering commonly used data sets, evaluation metrics and both traditional and deep learning-based algorithms. The study introduces a Python-based method for real-time object detection, utilizing deep learning architectures like Convolutional Neural Networks (CNNs) and Single Shot MultiBox Detector (SSD). Built on the open-source library OpenCV, the proposed system demonstrates high adaptability and ease of use through a transfer learning strategy. Experimental results validate the system's effectiveness in achieving accurate, real-time detection across various scenarios, including different lighting conditions and object occlusions. The study also discusses the current challenges, particularly small object detection, and outlines future research directions in the field.