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基于改进的SSD对绝缘体目标检测的轻量化方法的研究

Bing Zeng1, Yu Zhou1, Dilin He1

  • 1Nanchang Institute of Technology, Nanchang 330099, China.

Sensors (Basel, Switzerland)
|September 28, 2024
PubMed
概括
此摘要是机器生成的。

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Compositional Physical Reasoning of Objects and Events From Videos.

IEEE transactions on pattern analysis and machine intelligence·2025

本研究介绍了边缘设备的轻量级绝缘体检测算法,显著减少模型大小并提高处理速度,同时保持高精度. 增强的算法支持高效的边缘智能应用程序.

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 电气工程 电气工程

背景情况:

  • 边缘终端面临着大型,缓慢的绝缘体检测模型的挑战.
  • 有效地部署人工智能用于基础设施监控至关重要.

研究的目的:

  • 为边缘设备开发一种轻量级和快速的绝缘体检测算法.
  • 为了提高可见光绝缘体目标检测的准确性和效率.

主要方法:

  • 用幽灵模块网络取代VGG-16,用于轻量级的特征提取.
  • 集成的FPN+PAN和SimSPPF用于功能集成,以及scSE注意力机制.
  • 优化检测头,使用DIoU-NMS,通道修剪和知识蒸.

主要成果:

  • 模型参数从 26.15 M 减少到 0.61 M.
  • 计算负载从118.95G减少到1.49G.
  • 平均精度 (mAP) 从96.8%提高到98%.

结论:

  • 拟议的轻量级算法显著提高了检测速度,并减少了模型体积.
  • 与现有模型相比,实现了更高的性能,确保了检测准确度.
关键词:
这是SSD,SSD是SSD.道剪裁 道剪裁绝缘体的绝缘体是一个绝缘体轻量级的轻量级的轻量级的轻量级的目标检测 目标检测 目标检测

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  • 使用边缘智能为可见光绝缘体检测提供了可行的解决方案.