Deep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications
It provides insights for researchers, data scientists, and engineers applying AI-driven methodologies to large-scale object detection tasks, but it is incremental as it primarily reviews existing techniques.
This paper reviews state-of-the-art advancements in object detection and semantic segmentation, combining theoretical foundations with practical applications to bridge traditional methods and modern deep learning frameworks.
An in-depth exploration of object detection and semantic segmentation is provided, combining theoretical foundations with practical applications. State-of-the-art advancements in machine learning and deep learning are reviewed, focusing on convolutional neural networks (CNNs), YOLO architectures, and transformer-based approaches such as DETR. The integration of artificial intelligence (AI) techniques and large language models for enhancing object detection in complex environments is examined. Additionally, a comprehensive analysis of big data processing is presented, with emphasis on model optimization and performance evaluation metrics. By bridging the gap between traditional methods and modern deep learning frameworks, valuable insights are offered for researchers, data scientists, and engineers aiming to apply AI-driven methodologies to large-scale object detection tasks.