Computer Vision in the Deep Learning Era: A Survey of Architectures, Datasets, and Applications
In the deep learning era, computer vision has advanced quickly, radically changing how machines interpret and evaluate visual data. Data-driven deep neural networks that can immediately learn complicated representations from images have essentially replaced traditional computer vision algorithms that depended on manually created feature extraction. With an emphasis on important architectures including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and new hybrid models, this review article offers a thorough overview of contemporary deep learning-based computer vision. It also looks at popular benchmark datasets, such as ImageNet, COCO, and CIFAR, emphasizing how important they are for model building and assessment. The study also examines a variety of practical uses, from autonomous systems and facial recognition technology to object identification and medical picture analysis. Despite impressive advancements, a number of issues still exist, including as high processing demands, reliance on data, interpretability of models, and ethical issues with prejudice and privacy. The report concludes by outlining future research possibilities, with a focus on real-time deployment, multimodal learning, and efficient model construction. The purpose of this review is to offer an organized and perceptive overview of computer vision's present situation and promise in the deep learning age.