Comparative Study of Object Detection Techniques with Deep Convolutional Neural Networks

Object detection plays a role in computer vision. It has applications in different fields, including autonomous vehicles and medical image analysis. Recent advancements in neural networks (CNNs) have significantly improved object detection methods, resulting in remarkable enhancements in accuracy and speed. This study offers an overview of the developments in object identification techniques, specifically focusing on integrating deep CNNs. This study explores the evolution of detection methods from approaches to modern deep learning methods, conducts a comparative analysis of the state-of-the-art algorithms, and provides insights into their practical implementations and outcomes. Finally, the paper concludes by discussing research challenges and potential solutions related to object detection.