Object Detection Using YOLO

Object Detection Using YOLO

Sagar Srujan Somepalli, Vamsi Kishore Nallagopu, Ankitha Devi Gangavarapu, Sai Chaitanya Reddy Tiyyagura, Praveen Chowdary Vemasani

Thummala Sumanth Kumar

JNTU Anantpur

Abstract

Computer vision has a lot of interesting applications and object detection is one of the most interesting application. With the advance computer vision techniques, the objects present in the images can be identified in seconds with great accuracy. Hundreds of images can be processed in a few minutes to detect objects in those images. There are many algorithms available now through which this object detection can be performed very fastly. YOLO is one of these popular object detection methods.

In more traditional ML-based approaches, computer vision techniques are used to look at various features of an image, such as the color histogram or edges, to identify groups of pixels that may belong to an object. These features are then fed into a regression model that predicts the location of the object along with its label.

On the other hand, deep learning-based approaches employ convolutional neural networks and YOLO to perform end-to-end, unsupervised object detection, in which features don’t need to be defined and extracted separately.

Because deep learning methods have become the state-of-the-art approaches to object detection, these are the techniques we’ll be focusing on for the purpose of our research.