[Full Picture] 基于帧差结合卷积神经网络的运动面积分析的乳猪活动检测

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基于帧差结合卷积神经网络的运动面积分析的乳猪活动检测 - ScienceDirect

Source: sciencedirect.com

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Summary Analysis Research

Article summary:

1. This article proposes an automated method for monitoring piglet activity during the lactation period, combining frame difference analysis with a convolutional neural network (FD-CNN) and YOLOv5s network model to detect active piglet regions.

2. The ratio of active piglet area to all piglet area was determined in order to estimate the overall average activity of piglets during lactation.

3. Abnormal activities (activity values greater than 1) were traced back, and it was found that excessive activities were mainly caused by sows attacking piglets or hitting the limit.

Article analysis:

The article is generally reliable and trustworthy, as it provides detailed information about the proposed method for monitoring piglet activity during the lactation period, including its combination of frame difference analysis with a convolutional neural network (FD-CNN) and YOLOv5s network model to detect active piglet regions. The article also provides evidence for its claims, such as results showing that FD-CNN can detect active piglets (precision = 0.936), as well as results showing that when detection frequency is 6 s and 1 h, FD-CNN has similar activity values to manual detection at 58.36% and 78.90%, respectively.

However, there are some potential biases in the article which should be noted. For example, while the article does mention possible risks associated with using this method (e.g., interference from heating equipment), it does not provide any further details or explore counterarguments regarding these risks in depth. Additionally, while the article does present both sides of the argument regarding using this method for monitoring piglet activity during lactation, it does not present them equally; rather, it focuses more on presenting evidence in favor of using this method than exploring potential drawbacks or counterarguments against it.

Topics for further research:

Risks associated with using frame difference analysis for piglet activity monitoring Counterarguments against using frame difference analysis for piglet activity monitoring Advantages of using convolutional neural networks for piglet activity monitoring Disadvantages of using YOLOv5s network model for piglet activity monitoring Manual detection of piglet activity during lactation Impact of detection frequency on accuracy of piglet activity monitoring