Deep Learning Techniques for Image Recognition and Object Detection

¹ Associate Professor Department of Computer Science and Engineering Gitam(Deemed to be University) Visakhapatnam
² Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
³ Assistant Professor, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai – 127
⁴ College of technical engineering, The Islamic university, Najaf, Iraq
⁵ Tashkent State Pedagogical University, Tashkent, Uzbekistan
⁶ Department of mechanical Engineering, K. Ramakrishnan college of technology, Tiruchirappalli

sharada.narra@gmail.com
wmalghamdi@kau.edu.sa
k.karthika_civil@psvpec.in
ahmedalawadi@iunajaf.edu.iq
gulomova.nozima@mail.ru
vijayan.me@gmail.com

Abstract

Particularly in the fields of object identification and picture recognition, deep learning approaches have transformed the science of computer vision. This abstract provides a summary of recent developments and cutting-edge methods in deep learning for applications like object identification and picture recognition. The automated identification and classification of objects or patterns inside digital photographs is known as image recognition. Convolutional neural networks (CNNs), for example, have displayed outstanding performance in image identification tests. By directly learning hierarchical representations of visual characteristics from raw pixel data, these algorithms are able to recognize complex patterns and provide precise predictions. The ability for models to learn sophisticated visual representations straight from raw pixel data has transformed applications like object identification and picture recognition. The development of extremely accurate and effective systems has been accelerated by advances in deep learning architectures and large-scale annotated datasets. Further advances in object identification and picture recognition are anticipated as deep learning develops, with applications in a variety of fields including autonomous driving, surveillance, and medical imaging.