Object Detection Using Convolutional Neural Networks: A Review

Abstract

 The amount of data on the Internet has  increased dramatically as a result of the advent of  intelligent devices and social media. Object detection has  become a popular international study topic as an  important element of image processing. Convolutional  Neural Network’s (CNN) remarkable capacity with  feature learning and transfer learning has piqued attention  in the computer vision field in recent years, resulting in a  series of significant advancements in object identification.  As a result, it's an important study on how to use CNN to  improve object detection performance. The article began  by explaining the core concept and architecture of CNN.  Second, techniques for resolving current difficulties with  traditional object detection are examined, with a focus on  assessing detection algorithms based on region proposal  and regression. Finally, it provided various methods for  improving object detecting speed. The study then went on  to discuss various publicly available object identification  datasets as well as the notion of an assessment criterion.  Finally, it went over existing object detection research results and ideas, highlighting significant advancements  and outlining future prospects.