Object Detection using Machine Learning: A Survey

Abstract

ABSTRACT Computer vision techniques have become increasingly popular in the modern era. One of the applications of computer vision is in the detection of object in an image or video. In object detection a target object is detected, localized and labelled based on the type of object. Real time detection of objects in harsh environments, accuracy and speed are some of the major challenges in computer vision technology. A variety of algorithms have been proposed and implemented for the detection of objects and tracking of objects. In this paper an attempt is made to discuss and compare a few of the algorithms for object detection namely, You Only Look Once (YOLO), Region Convolution Neural Networks R-CNN and Viola-Jones. Detection of plastic waste and maritime life underwater by means of a Remote Operated Vehicle is the focus of object detection in this paper. Keywords: Object Detection, You Only Look Once (YOLO), Fast-Region Convolution Neural Network (Fast-RCNN), Faster-Region Convolution Neural Network (Faster RCNN), Viola Jones, Haar Features. Cite this Article: Rahul Hegde, Sanobar Patel, Rosha G Naik, Sagar K N, K. S. Shivaprakasha. Object Detection using Machine Learning: A Survey. International Journal of Embedded Systems and Emerging Technologies . 2019; 5(2): 7–11p.