ZIP基于Delphi与Pascal的YOLOv5深度目标检测与跟踪系统：支持多种推理引擎封装成DLL实现高效调用,基于Delphi与Pascal的YOLOv5和DeepSort目标检测跟踪系统，支持多种推 12.32MB

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  7. 本文将为您介绍如何使用实现目标检测和.docx 15.56KB
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  11. 目标检测目标跟踪支持和推理使用封装.html 2.78MB
  12. 编程实现与结合的目标检测与跟踪系统一引言随.html 2.78MB
  13. 随着计算机视觉和深度学习的快速发展目标检测和目标.docx 47.11KB

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基于Delphi与Pascal的YOLOv5深度目标检测与跟踪系统：支持多种推理引擎封装成DLL实现高效调用,基于Delphi与Pascal的YOLOv5和DeepSort目标检测跟踪系统，支持多种推理引擎并封装为DLL调用,delphi Pascal yolov5 deepsort 目标检测 目标跟踪，支持onnxruntime、dnn、openvino和tensorrt推理yolov5，使用c++封装成dll，delphi调用封装好的dll，实现目标检测和跟踪 ,核心关键词：yolov5; deepsort; 目标检测; 目标跟踪; onnxruntime; dnn; openvino; tensorrt推理; c++封装dll; delphi调用dll。,Delphi Pascal实现Yolov5与Deepsort目标检测与跟踪：DLL封装与调用教程
<link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90429812/2/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90429812/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">Delphi<span class="_ _0"> </span><span class="ff2">的跨越式编程：</span>Pascal<span class="_ _0"> </span><span class="ff2">编织下的深度追踪实践</span></div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">**<span class="ff2">摘要</span>**</div><div class="t m0 x1 h2 y3 ff2 fs0 fc0 sc0 ls0 ws0">这篇文章旨在分享如何将先进的<span class="_ _0"> </span><span class="ff1">Yolov5<span class="_"> </span></span>算法、<span class="_ _1"></span><span class="ff1">Deepsort<span class="_ _0"> </span><span class="ff2">目标跟踪技术，通过多种推理引擎</span></span></div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">(onnxruntime<span class="ff2">、<span class="_ _2"></span></span>dnn<span class="ff2">、<span class="_ _2"></span></span>openvino<span class="_"> </span><span class="ff2">和<span class="_ _0"> </span></span>tensorrt)<span class="_ _2"></span><span class="ff2">和<span class="_ _0"> </span></span>C++<span class="_ _2"></span><span class="ff2">封装<span class="_ _2"></span>的<span class="_ _0"> </span></span>DLL<span class="_"> </span><span class="ff2">技<span class="_ _2"></span>术，<span class="_ _2"></span>实现与<span class="_ _3"> </span></span>Delphi<span class="_"> </span><span class="ff2">编程<span class="_ _2"></span>环</span></div><div class="t m0 x1 h2 y5 ff2 fs0 fc0 sc0 ls0 ws0">境的深度整合。<span class="_ _4"></span>以不同的实例角度探索编程思路的灵活性，<span class="_ _4"></span>如何构建一种高效率且多变的解</div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">决方案。</div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">**<span class="ff2">一、从<span class="_ _0"> </span></span>Yolov5<span class="_"> </span><span class="ff2">到目标检测</span>**</div><div class="t m0 x1 h2 y8 ff2 fs0 fc0 sc0 ls0 ws0">在计算<span class="_ _2"></span>机视觉<span class="_ _2"></span>的领域<span class="_ _2"></span>中，目<span class="_ _2"></span>标检测<span class="_ _2"></span>是图像<span class="_ _2"></span>处理的<span class="_ _2"></span>重要一环<span class="_ _2"></span>。<span class="ff1">Yolov5<span class="_"> </span></span>的出现<span class="_ _2"></span>为这个<span class="_ _2"></span>领域带<span class="_ _2"></span>来</div><div class="t m0 x1 h2 y9 ff2 fs0 fc0 sc0 ls0 ws0">了革命性的进步。利用<span class="_ _0"> </span><span class="ff1">Yolov5<span class="_"> </span></span>进行目标检测，首先需要将<span class="_ _0"> </span><span class="ff1">Pascal<span class="_ _0"> </span></span>代码嵌入到我们的项目中</div><div class="t m0 x1 h2 ya ff2 fs0 fc0 sc0 ls0 ws0">去。以下是简单<span class="_ _2"></span>使用<span class="_ _0"> </span><span class="ff1">Yolov5<span class="_"> </span></span>进行目标检测的代码片段<span class="_ _2"></span>（这里我们省略具体<span class="_ _2"></span>参数和详细代码</div><div class="t m0 x1 h2 yb ff2 fs0 fc0 sc0 ls0 ws0">配置）<span class="_ _5"></span>：</div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">```delphi</div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">// Delphi<span class="_ _0"> </span><span class="ff2">代码示例（仅作为示例展示，具体需依据<span class="_ _0"> </span></span>Yolov5<span class="_"> </span><span class="ff2">的<span class="_ _0"> </span></span>Delphi<span class="_ _0"> </span><span class="ff2">接口实现）</span></div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">var Yolov5Detector: TObject; // <span class="_ _6"> </span><span class="ff2">假设这是我们封装好的<span class="_ _0"> </span></span>Yolov5<span class="_"> </span><span class="ff2">检测器对象</span></div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">// <span class="_ _6"> </span><span class="ff2">调用检测器进行图像检测</span></div><div class="t m0 x1 h2 y10 ff1 fs0 fc0 sc0 ls0 ws0">var DetectionResults: TArray&lt;DetectionInfo&gt;; // <span class="_ _6"> </span><span class="ff2">假设这是检测结果类型</span></div><div class="t m0 x1 h2 y11 ff1 fs0 fc0 sc0 ls0 ws0">DetectionResults := Yolov5Detector.Detect(InputImage); // <span class="_ _6"> </span><span class="ff2">输入图像为<span class="_ _0"> </span></span>InputImage</div><div class="t m0 x1 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">```</div><div class="t m0 x1 h2 y13 ff1 fs0 fc0 sc0 ls0 ws0">**<span class="ff2">二、</span>Deepsort<span class="ff2">：让目标跟踪更流畅</span>**</div><div class="t m0 x1 h2 y14 ff1 fs0 fc0 sc0 ls0 ws0">Deepsort<span class="_"> </span><span class="ff2">算法是目<span class="_ _2"></span>标跟踪<span class="_ _2"></span>领域的<span class="_ _2"></span>一颗明珠<span class="_ _2"></span>，它能<span class="_ _2"></span>够有效<span class="_ _2"></span>地将目<span class="_ _2"></span>标检测<span class="_ _2"></span>与跟踪<span class="_ _2"></span>算法结<span class="_ _2"></span>合在一</span></div><div class="t m0 x1 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">起。通过<span class="_ _6"> </span><span class="ff1">Deepsort<span class="_"> </span></span>算法，我们可以实现更精确、<span class="_ _1"></span>更流畅的目标跟踪。这里我们将<span class="_ _6"> </span><span class="ff1">Deepsort</span></div><div class="t m0 x1 h2 y16 ff2 fs0 fc0 sc0 ls0 ws0">算法与<span class="_ _0"> </span><span class="ff1">Yolov5<span class="_"> </span></span>的检测结果相结合，形成完整的跟踪系统。</div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">```c++</div><div class="t m0 x1 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0">// C++<span class="ff2">代码示例（用于<span class="_ _0"> </span></span>DLL<span class="_"> </span><span class="ff2">封装）</span></div><div class="t m0 x1 h2 y19 ff1 fs0 fc0 sc0 ls0 ws0">// <span class="_ _6"> </span><span class="ff2">假设我们有一个<span class="_ _0"> </span></span>DeepsortTracker<span class="_"> </span><span class="ff2">类，用于处理跟踪逻辑</span></div><div class="t m0 x1 h2 y1a ff1 fs0 fc0 sc0 ls0 ws0">class DeepsortTracker {</div><div class="t m0 x1 h2 y1b ff1 fs0 fc0 sc0 ls0 ws0"> <span class="_ _7"> </span>// ... <span class="_ _6"> </span><span class="ff2">实现代码</span> <span class="_ _6"> </span>...</div><div class="t m0 x1 h2 y1c ff1 fs0 fc0 sc0 ls0 ws0"> <span class="_ _7"> </span>std::vector&lt;TrackedObject&gt; TrackObjects(const std::vector&lt;Detection&gt;&amp; detections);</div><div class="t m0 x1 h2 y1d ff1 fs0 fc0 sc0 ls0 ws0">};</div><div class="t m0 x1 h2 y1e ff1 fs0 fc0 sc0 ls0 ws0">```</div><div class="t m0 x1 h2 y1f ff1 fs0 fc0 sc0 ls0 ws0">**<span class="ff2">三、跨平台推理引擎与<span class="_ _0"> </span></span>DLL<span class="_"> </span><span class="ff2">封装</span>**</div><div class="t m0 x1 h2 y20 ff2 fs0 fc0 sc0 ls0 ws0">在封装<span class="_ _0"> </span><span class="ff1">DLL<span class="_"> </span></span>时，我<span class="_ _2"></span>们利用多种<span class="_ _2"></span>推理引擎（<span class="_ _2"></span>如<span class="_ _0"> </span><span class="ff1">onnxruntime</span>、<span class="ff1">dnn<span class="_ _2"></span></span>、<span class="ff1">openvino<span class="_"> </span></span>和<span class="_ _6"> </span><span class="ff1">tensorrt</span>）<span class="_ _2"></span>作</div><div class="t m0 x1 h2 y21 ff2 fs0 fc0 sc0 ls0 ws0">为后端支持，<span class="_ _4"></span>以提高兼容性和性能。<span class="_ _4"></span>这样可以在不同硬件上运行并选择最佳的推理引擎来满</div><div class="t m0 x1 h2 y22 ff2 fs0 fc0 sc0 ls0 ws0">足项目需求。以下是<span class="_ _0"> </span><span class="ff1">C++</span>封装成<span class="_ _0"> </span><span class="ff1">DLL<span class="_"> </span></span>的示例：</div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>
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