The Impact of Deep Learning on Computer Vision: From Image Classification to Scene Understanding

Abstract

Deep learning has significantly advanced the field of computer vision, transitioning from simple image classification tasks to more complex scene understanding and object detection applications. Convolutional Neural Networks (CNNs), in particular, have played a crucial role in this transformation, enabling machines to achieve unprecedented accuracy in visual data interpretation. This article explores the evolution of deep learning in computer vision, tracing the development of CNN architectures, from early models like AlexNet to more sophisticated networks such as ResNet. We delve into the progression from image classification to advanced tasks like object detection, segmentation, and scene understanding, highlighting their impact across industries, including healthcare, autonomous vehicles, and retail. Furthermore, the article addresses the ethical challenges posed by these technologies, such as bias, privacy concerns, and the need for accountability. By examining the technological advancements and their broader implications, this article provides a comprehensive overview of the current state of deep learning in computer vision and its potential future directions.