Multi Object Detection
The study explores the underlying architecture of CNNs, elucidating how convolution, pooling, and flattening layers enable efficient image processing and object identification.
Abstract
Abstract: In the realm of computer vision, the ability to accurately detect and comprehend objects within images and videos is of paramount importance. This research is dedicated to advancing the field of object detection, a critical component of computer vision, with a particular focus on leveraging Convolutional Neural Networks (CNNs) to enhance accuracy. CNNs have revolutionized object recognition tasks, outperforming traditional methods like Viola-Jones, SIFT, and HOG. The study explores the underlying architecture of CNNs, elucidating how convolution, pooling, and flattening layers enable efficient image processing and object identification. Object detection holds immense practical significance, spanning applications such as autonomous vehicles, surveillance, and medical imaging. By delving into the intricacies of CNNs and their role in object detection, this research contributes to the ongoing evolution of computer vision, promising advancements in diverse sectors of industry and technology.