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相关概念视频

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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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相关实验视频

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HGCS-Det:一种基于深度学习的解决方案,用于在复杂的场景中定位和识别家庭垃圾.

Houkui Zhou1,2, Chang Chen1, Zhongyi Xia1

  • 1College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou 311300, China.

Sensors (Basel, Switzerland)
|June 27, 2025
PubMed
概括

本研究介绍了HGCS-Det,这是一种用于在复杂环境中准确检测垃圾的深度学习模型. 它实现了高精度和实时性能,改进了废物管理系统.

关键词:
滑动损失 滑动损失注意的特点是融合融合.垃圾检测器 垃圾检测器一个实例的边界增强.正常化注意力注意力正常化

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 环境技术 环境技术

背景情况:

  • 深度学习为智能垃圾检测和分类提供了潜力.
  • 复杂的环境和不规则的垃圾特征对当前的检测方法构成重大挑战.
  • 现有的模型在复杂的垃圾检测场景中经常在精度和实时性能之间做出妥协.

研究的目的:

  • 提出一种新的深度学习模型,HGCS-Det,用于在具有挑战性的环境中进行强大的垃圾检测.
  • 提高垃圾检测系统的精度和实时功能.
  • 为现实世界垃圾分类和管理提供实用解决方案.

主要方法:

  • 开发了基于YOLOv8的HGCS-Det,结合了正常化注意模块来减少噪音干扰.
  • 集成了一个注意力功能融合模块,以优化频道注意力权重.
  • 采用一个实例边界增强模块用于细粒度特征提取和一个滑动损失功能来改善硬样本识别.

主要成果:

  • 在HGI30数据集上,HGCS-Det实现了93.6%的平均平均精度 (mAP) 和每秒86 (FPS).
  • 该模型显示,与YOLOv12相比,mAP高3.33%,参数增加最小 (3.02M).
  • 在检测效率和适用性方面都超过了最先进的方法.

结论:

  • 在复杂的场景中,HGCS-Det为垃圾检测提供了一个轻量级但准确的解决方案.
  • 该模型的实时性能和增强的准确性使其适合嵌入式系统和实际废物管理.
  • 这项研究为垃圾分类系统的工程应用提供了宝贵的技术参考.