Deep Learning Based Computer Vision

Deep learning-based computer vision uses artificial neural networks, primarily convolutional neural networks (CNNs) and transformers, to analyze images and videos, enabling automated object recognition, classification, and scene understanding. Current research emphasizes improving model efficiency for resource-constrained environments (e.g., edge devices), enhancing robustness through techniques like data augmentation and uncertainty quantification, and developing more interpretable models. This field is significantly impacting various sectors, including manufacturing (defect detection), assistive technologies (robotic mobility aids), and urban planning (monitoring housing changes), by automating tasks and providing valuable insights from visual data.