Deep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications

TL;DR

This paper reviews recent advances in object detection and semantic segmentation, integrating theoretical insights with practical applications, focusing on CNNs, YOLO, transformers, and AI techniques for large-scale tasks.

Contribution

It provides a comprehensive analysis of modern deep learning methods and their applications in object detection and segmentation, bridging traditional and AI-driven approaches.

Findings

State-of-the-art CNN, YOLO, and transformer models improve detection accuracy.

AI techniques and large language models enhance performance in complex environments.

Model optimization and evaluation metrics are crucial for large-scale applications.

Abstract

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.