March 2025

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实时目标检测：YOLO 算法基础介绍 摘要：目标检测是计算机视觉领域的核心任务之一，旨在识别图像或视频中存在的目标并确定其位置。随着深度学习的发展，目标检测算法取得了显著进步。然而，在许多实际应用场景中，如自动驾驶、实时监控、机器人导航等，不仅要求高精度，更要求极高的处理速度，即实时性。YOLO（You Only Look Once）系列算法正是为满足这一需求而设计的里程碑式工作。本文将深入探讨 YOLO 算法的基础原理、核心思想、关键技术、演进历程及其在实时目标检测领域的深远影响。 关键词：目标检测，实时检测，深度学习，计算机视觉，YOLO，卷积神经网络，Bounding Box，非极大值抑制 1. 引言 1.1 目标检测任务概述 计算机视觉旨在让机器能够“看懂”世界。其中，目标检测（Object Detection）是理解图像内容的基础且关键的一步。与图像分类（识别图像中包含什么类别）不同，目标检测不仅需要识别出图像中包含哪些物体（Classification），还需要精确定位这些物体在图像中的位置（Localization）。通常，这个位置信息由一个紧密包围目标的边界框（Bounding Box, BBox）来表示，同时给出该边界框内物体所属的类别及其置信度（Confidence Score）。 1.2

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Understanding MinIO: The Open Source S3 Storage Solution In the rapidly evolving landscape of data storage, the rise of cloud-native