A Comprehensive Review of Deep Learning Architectures and Their Applications in Computer Vision

Abstract

The rapid advancements in deep learning have revolutionized the field of computer vision, enabling remarkable progress in tasks such as image recognition, object detection, semantic segmentation, and video analysis. Deep learning architectures, particularly neural networks, have emerged as the backbone of state-of-the-art solutions for complex vision tasks. Among these, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and transformers have proven highly effective in extracting meaningful patterns from visual data. This comprehensive review explores the evolution of deep learning architectures and their wide-ranging applications in computer vision.

The paper begins by outlining the fundamental principles of deep learning and its relevance to visual data processing. It provides an in-depth discussion of the key architectures, starting with the basic neural networks and advancing to more complex models such as CNNs, RNNs, and attention-based transformers. Special attention is given to the hierarchical feature extraction capabilities of CNNs, which make them indispensable in computer vision. Furthermore, the review highlights the advent of GANs and transformers, which have opened new frontiers in generative modeling and large-scale vision tasks, respectively.

The paper also categorizes and examines the diverse applications of deep learning in computer vision, including medical imaging, autonomous vehicles, surveillance systems, augmented reality, and remote sensing. It delves into how deep learning has transformed traditional approaches, yielding better accuracy and efficiency. Several optimization strategies, such as data augmentation, transfer learning, and model pruning, are discussed to highlight their role in enhancing performance.

Finally, the review explores the challenges and future trends in deep learning for computer vision. Issues such as computational demands, data dependency, interpretability, and fairness are examined. The paper concludes by emphasizing the growing need for interdisciplinary research to further advance the field and make deep learning more accessible and efficient across diverse domains.