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.

翻译：本文对目标检测与语义分割进行了深入探讨，将理论基础与实际应用相结合。综述了机器学习和深度学习领域的最新进展，重点关注卷积神经网络（CNNs）、YOLO架构以及基于Transformer的方法（如DETR）。研究了人工智能（AI）技术与大语言模型在复杂环境中增强目标检测的融合应用。此外，对大数据处理进行了全面分析，重点探讨了模型优化与性能评估指标。通过弥合传统方法与现代深度学习框架之间的差距，为致力于将AI驱动方法应用于大规模目标检测任务的研究人员、数据科学家和工程师提供了宝贵的见解。