综述目标检测与语义分割的理论与应用，涵盖CNN、YOLO和DETR等主流方法。
Deep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications
- 基于CNN、YOLO与DETR等模型，系统梳理检测与分割技术演进
- 分析大模型与语言模型在复杂环境下的检测增强效果
- 适合关注AI落地与模型优化的研究者与工程师参考
本文深入探讨了目标检测与语义分割，融合理论基础与实际应用。综述了机器学习与深度学习领域的最新进展，重点聚焦卷积神经网络（CNN）、YOLO架构以及基于变压器的DETR等方法。研究还考察了人工智能技术与大语言模型在复杂环境下提升目标检测性能的整合应用。此外，对大数据处理进行了全面分析，强调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，为研究人员、数据科学家和工程师提供在大规模目标检测任务中应用AI驱动方法的宝贵见解。
原文摘要 · Abstract (English)
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.
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