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相关概念视频

Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Multiple Comparison Tests

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Parallel Processing01:20

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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相关实验视频

Updated: Jun 27, 2025

Noninvasive, In-pen Approach Test for Laboratory-housed Pigs
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基于改进的YOLOv7与DeepSORT结合的动态猪计数方法的研究

Xiaobao Shao1, Chengcheng Liu1, Zhixuan Zhou1

  • 1College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China.

Animals : an open access journal from MDPI
|April 27, 2024
PubMed
概括

这项研究引入了一种改进的YOLOv7模型与DeepSORT,用于在复杂环境中准确,实时的猪计数. 改进的模型实现了高精度和速度,这对于自动化大规模农业至关重要.

关键词:
这是一个DeepSORT.这就是PConvv.这就是YOLOv7的意义.注意力机制注意力机制动态计数计数 动态计数计数猪计数 猪计数 猪计数 猪计数

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科学领域:

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

背景情况:

  • 准确的猪库存对于精准农业至关重要.
  • 在复杂的猪舍中,自动计数具有挑战性,因为存在障碍和猪的行为.
  • 现有的深度学习方法通常依赖于静态图像或上空视图,限制了现实世界的应用.

研究的目的:

  • 开发一种基于视频的可靠动态计数方法,用于复杂环境中的猪.
  • 增强YOLOv7对象检测模型,以提高猪计数的准确性和效率.
  • 将YOLOv7与DeepSORT集成,用于实时跟踪和计数.

主要方法:

  • 优化了使用PConv的YOLOv7架构,以减少计算和提高推理速度.
  • 集成的协调注意力 (CA) 机制,以提高角和强度的感知.
  • 结合了增强的YOLOv7和DeepSORT,用于基于视频的动态猪计数.

主要成果:

  • 与原始模型相比,改进的YOLOv7在斜面,顶部和组合数据集中实现了更高的mAP.
  • 在目标检测中表现出优越的性能与YOLOv5,YOLOv4,YOLOv3,更快的RCNN和SSD相比.
  • 在动态计数实验中,YOLOv7-DeepSORT系统在22 FPS中实现了96.58%的平均精度.

结论:

  • 拟议的动态计数方法有效地解决了复杂环境中自动猪计数的挑战.
  • 增强的YOLOv7-DeepSORT模型为大规模养殖中实时,准确的猪库存提供了可行的解决方案.
  • 这项研究提供了有价值的数据,并为推进自动猪计数技术提供了参考.