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Streamlining YOLOv7 for Rapid and Accurate Detection of Rapeseed Varieties on Embedded Device.

Siqi Gu1,2, Wei Meng1,2, Guodong Sun1,2

  • 1School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China.

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Summary

This study introduces a lightweight YOLOv7 model for real-time rapeseed seed detection on embedded devices. The optimized model significantly reduces parameters and inference time while maintaining high accuracy for precision agriculture.

Keywords:
embedded deviceinference speedpruning strategyrapeseed detection

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Area of Science:

  • Computer Vision
  • Agricultural Technology
  • Deep Learning

Background:

  • Real-time seed detection is crucial for agriculture, but existing methods struggle with accuracy or embedded device deployment.
  • Traditional approaches often lack the efficiency required for resource-constrained agricultural hardware.

Purpose of the Study:

  • To develop an efficient, real-time seed variety detection model suitable for embedded devices.
  • To optimize the YOLOv7 object detection model for agricultural applications, specifically rapeseed detection.

Main Methods:

  • A dual-dimensional pruning technique (spatial and channel) was applied to the YOLOv7 model.
  • Spatial pruning effectiveness was experimentally validated.
  • Custom ratio layer-by-layer channel pruning was selected for optimal performance.

Main Results:

  • The pruned YOLOv7 model achieved an mAP of 96.89%, an increase from 96.68%.
  • Model parameters were reduced from 36.5 M to 9.19 M.
  • Inference time on Raspberry Pi 4B decreased from 4.48 s to 1.18 s.

Conclusions:

  • The proposed pruned YOLOv7 model is highly suitable for deployment on embedded devices.
  • The model enables accurate and efficient real-time rapeseed detection in diverse agricultural settings.
  • This advancement supports precision agriculture through improved on-device analytics.