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YOLOv8 forestry pest recognition based on improved re-parametric convolution.

Lina Zhang1, Shengpeng Yu1, Bo Yang2

  • 1College of Information Technology, Jilin Agricultural University, Changchun, China.

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Summary
This summary is machine-generated.

A new lightweight forestry pest detection algorithm, RSD-YOLOv8, improves accuracy by 4.2% while reducing model size by 33%. This efficient model is ideal for resource-limited environments, aiding sustainable forest management.

Keywords:
HGNetv2YOLOv8heavy parameter lightweight convolutionmodel pruningpest identification

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Area of Science:

  • Forestry
  • Computer Vision
  • Artificial Intelligence

Background:

  • Forest pests pose significant ecological and economic threats, especially in remote areas.
  • Traditional pest detection methods struggle with accuracy and efficiency in complex, resource-limited environments.
  • There is a critical need for enhanced pest detection solutions that are both accurate and computationally efficient.

Purpose of the Study:

  • To develop an improved lightweight algorithm for forestry pest detection.
  • To address the challenges of pest detection in resource-constrained settings.
  • To enhance the efficiency and accuracy of pest detection systems.

Main Methods:

  • Proposed the RSD-YOLOv8 algorithm, an enhanced version of YOLOv8.
  • Introduced RepLightConv for a more parameter-efficient backbone (Rep-HGNetV2).
  • Integrated a slim-neck structure, Dyhead module, and applied model pruning for further lightweighting.

Main Results:

  • RSD-YOLOv8 achieved a Map@0.5:0.95 of 88.6%, a 4.2% improvement over YOLOv8.
  • Reduced model parameters by approximately 36%.
  • Decreased model size by 33% and operations by 36%, enhancing computational efficiency.

Conclusions:

  • The RSD-YOLOv8 model effectively enhances pest detection accuracy while minimizing resource requirements.
  • Its efficiency in remote, resource-limited areas makes it highly practical for real-world applications.
  • This advancement supports intelligent and sustainable development in agroforestry ecology.