Deep Learning Techniques for Image Recognition and Object Detection

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.