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    <journal-meta>
      <journal-title-group><journal-title>南太湖新工科产业学报</journal-title></journal-title-group>
      <issn>3136-2249</issn>
      <publisher><publisher-name>求真学术出版</publisher-name></publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">53e60f86-8224-4a1a-a249-4bf88caf99db</article-id>
      <article-id pub-id-type="doi">10.68249/jstnei.2026.0014</article-id>
      <elocation-id>0014</elocation-id>
      <title-group><article-title>排爆车智能视觉目标检测系统</article-title></title-group>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes"><name><surname>白紫婵</surname></name><aff>北华航天工业学院电子与控制工程学院</aff></contrib>
        <contrib contrib-type="author"><name><surname>卫曦</surname></name><aff>北华航天工业学院电子与控制工程学院</aff></contrib>
        <contrib contrib-type="author"><name><surname>葛景仲</surname></name><aff>北华航天工业学院电子与控制工程学院</aff></contrib>
      </contrib-group>
      <pub-date publication-format="electronic"><year>2026</year><month>8</month><day>29</day></pub-date>
      <volume>1</volume><issue>2</issue>
      <fpage>6</fpage><lpage>12</lpage>
      <abstract><p>对复杂环境而言，排爆车所作的作业要同时完成两项任务：查清爆炸物、确定机械臂可用的位置。文中将实时检测同6D位姿估计结合起来，设计为车载视觉方案。检测部分是经过改良的YOLOv5s——在SPPF之前利用坐标注意力、把边界框回归损失由CIOU改作EIOU，以类别及边界框形式给出结果；判断为高威胁的物体按框截取区域后即用FoundationPose处理，结合已知三维模型的特征做6自由度位姿解算。所涉两个模块用优先级异步调度器连接。检测端针对未爆弹数据所获的指标是精确率85.3%、召回率67.7%、mAP77.0%，比原版YOLOv5s各项数值分别多0.7、1.4、1.1个点，其结果满足机械臂自动定位的需要，验证了该方法的可行性与效率。</p></abstract>
      <kwd-group><kwd>排爆车</kwd><kwd>目标检测</kwd><kwd>YOLOv5s</kwd><kwd>6D姿态估计</kwd><kwd>FoundationPose</kwd></kwd-group>
      <permissions><license><license-p>CC BY-NC-ND 4.0</license-p></license></permissions>
      <self-uri xlink:href="https://www.qiuzhenpress.com/articles/jstnei-2026-0014" />
    </article-meta>
  </front>
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