Convolutional Neural Networks for Object Detection and Recognition

Ms. Archana Karne; Mr. RadhaKrishna Karne; Mr. V. Karthik Kumar; Dr. A. Arunkumar

doi:10.55529/jaimlnn.32.1.13

Authors

Ms. Archana Karne UG Student, CMR Technical Campus, Hyderabad, India
Mr. RadhaKrishna Karne Assistant Professor in ECE, CMR Institute of Technology, Hyderabad, India
Mr. V. Karthik Kumar Assistant Professor in ECE, BITS Narsampet, Hyderabad, India
Dr. A. Arunkumar Professor in CSE, MLRITM, Hyderabad, India

Keywords:

Convolutional Neural Network (CNN), Deep Learning (DL), Object Detection, Deep Neural Networks (DNN), Object Recognition, YOLO, Object Tracking, Object Classification.

Abstract

One of the essential technologies in the fields of target extraction, pattern recognition, and motion measurement is moving object detection. Finding moving objects or a number of moving objects across a series of frames is called object tracking. Basically, object tracking is a difficult task. Unexpected changes in the surroundings, an item's mobility, noise, etc., might make it difficult to follow an object. Different tracking methods have been developed to solve these issues. This paper discusses a number of object tracking and detection approaches. The major methods for identifying objects in images will be discussed in this paper. Recent years have seen impressive advancements in fields like pattern recognition and machine learning, both of which use convolutional neural networks (CNNs). It is mostly caused by graphics processing units'(GPUs) enhanced parallel processing capacity. This article describes many kinds of object classification, object racking, and object detection techniques. Our results showed that the suggested algorithm can detect moving objects reliably and efficiently in a variety of situations.