• P. NAGARAJU
• Dr. Manchala Sadanandam

Recent years have seen deep neural networks emerge as the most significant advancement in the field of computer vision (CV), producing outstanding results for picture categorization. Convolution neural networks (CNNs) are thought to be an intriguing tool for researching biological vision since this class of artificial vision systems has visual identification abilities that are comparable to those of human observers. In the world of Internet applications nowadays, CV technology has taken on significant importance. Object detection (OD) has evolved into one of the fundamental issues in CV and is now the foundation of many vision tasks. It offers reliable data, whether we want to understand how pictures and text interact or identify precise classifications. The advancement of OD networks is examined in this article. We provide candidate region-based OD, including R-CNN, Retina Net, YOLO, and SSD, etc. Currently, detectors are the best approach. It offers recommendations for the future development trend and OD research by examining the existing research state of OD networks. We are concentrating on the fundamental components of the architecture of CNN in this article and CNN-based OD techniques are presented.

Deep Learning (DL), Convolutional Neural Network (CNN), Object Detection (OD), Computer Vision (CV), object recognition (OR), Machine learning (ML), Neural Network (NN).

"A Perspective Review On Object Detection Using Computer Vision", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.e811-e818, July-2023, Available :http://www.jetir.org/papers/JETIR2307488.pdf

"A Perspective Review On Object Detection Using Computer Vision", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppe811-e818, July-2023, Available at : http://www.jetir.org/papers/JETIR2307488.pdf