Paper Key : IRJ************051
Author: Anil Naresh Yadav,Pavan Mishra
Date Published: 03 Jan 2025
Abstract
Object detection plays a pivotal role in advancing computer vision by enabling machines to interpret and analyze visual data effectively. With its versatile functionality and extensive library, OpenCV emerges as a cornerstone for implementing cutting-edge object detection techniques. This comprehensive review delves into the methodologies, advancements, and applications of object detection using OpenCV. The paper systematically explores traditional algorithms such as Haar cascades and HOG-SVM, alongside modern deep learning-based approaches like YOLO, SSD, and Faster R-CNN, emphasizing their integration with OpenCV for real-time performance. It further highlights OpenCV's optimization capabilities, including GPU acceleration and model conversion for efficient deployment. Practical applications, challenges, and future research directions in leveraging OpenCV for robust and scalable object detection are discussed. This review aims to serve as a valuable resource for researchers and practitioners seeking to harness OpenCV's potential in developing intelligent visual systems.