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相关概念视频

Survival Tree01:19

Survival Tree

51
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
51

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[Study on determination of eight metal elements in Hainan arecanut leaf by flame atomic absorption spectrophotometry].

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Pseudonocardia endophytica sp. nov., isolated from the pharmaceutical plant Lobelia clavata.

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Highly selective biotransformation of ginsenoside Rb1 to Rd by the phytopathogenic fungus Cladosporium fulvum (syn. Fulvia fulva).

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Synthesis and resolution of planar-chiral ruthenium-palladium complexes with ECE' pincer ligands.

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Human DNA sequences: more variation and less race.

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DSPE-ViT: a lightweight vision transformer with dynamic sparse positional encoding for dense small object detection in UAV imagery.

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ST-HONet: Spatio-Temporal Hierarchical Network for long-horizon bimanual visuomotor imitation.

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ST-HADP: Spatio-Temporal hierarchical attention diffusion policy for long-horizon generalizable bimanual visuomotor imitation.

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EQISP: efficient quantized image signal processing with multi-scale pyramid fusion for resource constrained embodied perception.

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Research on embodied agent multimodal perception and real-time path planning algorithms for complex unstructured environments.

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NL-YOLOv5: a model with a larger receptive field and the ability to globally acquire features.

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相关实验视频

Updated: May 27, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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一个可扩展的多模式学习果实检测算法用于动态环境.

Liang Mao1,2, Zihao Guo1, Mingzhe Liu2

  • 1Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence Application Technology Research Institute, Shenzhen Polytechnic University, Shenzhen, China.

Frontiers in neurorobotics
|February 21, 2025
PubMed
概括
此摘要是机器生成的。

这项研究介绍了YOLOv5-Litchi,这是一种用于检测果的新方法,显著提高了对小,封闭的目标的准确性和回忆. 改进的模型为产量估计提供了更好的果实检测.

关键词:
深度学习是一种深度学习.果实识别功能 果实识别功能机器学习是机器学习.多模式学习是多模式学习.客观检测的目标检测.

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科学领域:

  • 计算机视觉 计算机视觉
  • 农业技术 农业技术
  • 机器学习 机器学习

背景情况:

  • 在自然场景中检测李奇果是具有挑战性的,因为密集的遮和小目标大小.
  • 现有的方法在复杂的农业环境中难以准确识别和定位.

研究的目的:

  • 开发一种新的多式联运目标检测方法,YOLOv5-Litchi,用于增强果的检测.
  • 为了解决密集封闭和小目标识别在李奇果检测中的局限性.

主要方法:

  • 修改了YOLOv5s架构,简化了FPN结构和增加了检测头 (5).
  • 集成的TSCD检测头针对小目标,并用EIoU和VFLoss取代损失功能.
  • 利用滑动切片方法来改进小目标预测.

主要成果:

  • 与原来的YOLOv5s相比,YOLOv5-Litchi在准确度方面取得了9.5%,回忆率为0.9%,平均平均精度 (mAP) 提高了12.3%.
  • 性能优于YOLOx,YOLOv6和YOLOv8,具有更高的AP值 (分别为4.0%,6.3%和3.7%).

结论:

  • 增强的网络显著改善了回忆和AP,减少了错过的检测,提高了预测框架的准确性.
  • YOLOv5-Litchi有效地处理密集的遮和小目标,证明适合于litchi水果检测和产量估计.