This book offers an in-depth exploration of object detection and semantic segmentation, combining theoretical foundations with practical applications. It covers state-of-the-art advancements in machine learning and deep learning, with a focus on convolutional neural networks (CNNs), YOLO architectures, and transformer-based approaches like DETR. The book also delves into the integration of artificial intelligence (AI) techniques and large language models for enhanced object detection in complex environments. A thorough discussion of big data analysis is presented, highlighting the importance of data processing, model optimization, and performance evaluation metrics. By bridging the gap between traditional methods and modern deep learning frameworks, this book serves as a comprehensive guide for researchers, data scientists, and engineers aiming to leverage AI-driven methodologies in large-scale object detection tasks.

本书探讨目标检测与语义分割的理论基础与实际应用，填补了传统方法与现代深度学习框架之间的空白。书中介绍了卷积神经网络、YOLO架构和基于变换器的方法等先进技术，强调了人工智能与大语言模型在复杂环境中的目标检测中的集成。通过对大数据分析的深入讨论，展示了数据处理、模型优化和性能评估的重要性。

深度学习与机器学习——目标检测与语义分割：从理论到应用