• Pradnya Abhay Muley
• Km Alankrata

This review paper presents a comprehensive analysis of four prominent object detection algorithms: Convolutional Neural Networks (CNN), Region-based CNN (R-CNN), Fast R-CNN, and You Only Look Once (YOLO). Each algorithm's architecture, methodology, strengths, and limitations are explored in detail The study aims to provide insights into the strengths, weaknesses, and applicability of each algorithm for image recognition tasks. Through a systematic review of literature and empirical evaluations, we highlight the unique characteristics, architectures, and training methodologies of these algorithms. This evaluation provides a useful reference for researchers, professionals, and enthusiasts interested in exploring object detection algorithms and making well-informed choices when selecting algorithms for various applications.

Object Detection, CNN, Region-based CNN, Fast R-CNN, and You Only Look Once, YOLO.

"Exploring Object Detection Algorithms: A Comparative Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.h813-h816, July-2024, Available :http://www.jetir.org/papers/JETIR2407792.pdf

"Exploring Object Detection Algorithms: A Comparative Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. pph813-h816, July-2024, Available at : http://www.jetir.org/papers/JETIR2407792.pdf