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相关概念视频

Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Classification of Signals01:30

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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Extraction: Advanced Methods00:56

Extraction: Advanced Methods

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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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相关实验视频

Updated: Jul 4, 2025

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
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鱼声纳图像识别算法基于改进的YOLOv5图像识别算法.

Bowen Xing1, Min Sun1, Minyang Ding2

  • 1College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China.

Mathematical biosciences and engineering : MBE
|February 2, 2024
PubMed
概括
此摘要是机器生成的。

这项研究使用增强的YOLOv5算法改进了鱼声纳图像检测,提高了可持续渔业管理的准确性. 新方法有效地识别鱼类种群,有助于海洋资源评估和保护工作.

关键词:
这是YOLOv5的.算法优化的算法优化深度学习是一种深度学习.鱼类检测 鱼类检测 鱼类检测索纳尔 (sonar) 是一种声纳仪.

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科学领域:

  • 海洋生物学 海洋生物学
  • 计算机视觉 计算机视觉
  • 渔业 科学 渔业 科学

背景情况:

  • 可持续的渔业管理依赖于准确的鱼类库存评估.
  • 过度捕捞和深海评估的挑战需要先进的检测方法.
  • 声纳图像噪声会降低鱼的目标特征,降低物体检测精度.

研究的目的:

  • 引入基于YOLOv5.5的改进的鱼声纳图像检测算法.
  • 在杂的声纳图像中增强特征提取和小目标检测.
  • 提高海洋渔业鱼类种群评估的准确性和效率.

主要方法:

  • 将一个C3N模块与深度可分离的卷积和反向瓶层集成到YOLOv5.5中.
  • 引入了一个小写的浅特征层,以增强更大的像素的提取.
  • 结合正常化加权距离与交叉在欧盟 (IoU) 和取代非最大抑制 (NMS) 软-NMS.

主要成果:

  • 改进的YOLOv5模型在精度 (2.3%),回忆 (4.7%) 和平均平均精度 (2.7%) 方面比原始模型显著提高.
  • 与YOLOv3相比,增强型模型在精度 (2.5%),回忆 (6.3%) 和平均精度 (6.7%) 中取得了更大的改进.
  • 该方法有效地提高了声纳图像检测的准确性,特别是对于小型和重叠的目标.

结论:

  • 增强的YOLOv5算法显著提高了鱼声纳图像检测准确度.
  • 这种方法为鱼类种群评估和海洋资源评估提供了一个有前途的工具.
  • 无人驾驶水下车辆的进步可以利用这项技术来改善渔业管理和鱼类养殖决策.