Neural Network Architectures for Real-Time Image and Video Processing Applications
DOI:
https://doi.org/10.18034/ei.v10i2.735Keywords:
Neural Networks, Real-Time Image Processing, Video Processing, Deep Learning, Convolutional Neural Networks (CNNs), Object Detection, Video AnalyticsAbstract
This research optimizes neural network topologies for real-time image and video processing to achieve high-speed, accurate performance in dynamic contexts. The project aims to find efficient optimization methodologies, track neural network model progress, and highlight visual media applications. A secondary data review synthesizes peer-reviewed literature, technical reports, neural network design, and optimization advances. The research found that lightweight neural network architectures like MobileNet and Transformer-based Vision Transformers (ViTs) boost the computing economy without losing accuracy. Real-time applications need model pruning, quantization, knowledge distillation, and hardware-aware design. From real-time object identification in surveillance and autonomous driving to medical imaging and creative media creation, neural networks have transformed many applications. Despite these advances, balancing accuracy and economy, addressing hardware variability, and assuring ethical usage in face recognition remain issues. The report emphasizes the need for privacy-friendly and egalitarian AI rules. These results may help future research improve real-time visual processing systems and legislators control their responsible use in real-world applications.
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Copyright (c) 2022 Deekshith Narsina; Nicholas Richardson; Arjun Kamisetty; Jaya Chandra Srikanth Gummadi; Krishna Devarapu
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