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An Efficient Lightweight Object Detection Algorithm for Defect Identification in Paeoniae Radix Alba With Rapid Area

Miao Huan1, Tao Wang1, Qin Xu1

  • 1Huzhou University Huzhou China.

Food Science & Nutrition
|September 17, 2025
PubMed
Summary

This study introduces an advanced algorithm for inspecting Paeoniae Radix Alba (white peony root) quality. It enhances defect detection and physical dimension measurement, crucial for food and medicinal applications.

Keywords:
FCALSD‐HeadOpenCVPaeoniae Radix AlbaYOLOv8traditional Chinese medicinal

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Area of Science:

  • Traditional Chinese Medicine
  • Agricultural Science
  • Computer Vision

Background:

  • Paeoniae Radix Alba (white peony root, WP) is a vital herb in traditional Chinese medicine, food, and health supplements.
  • Ensuring the quality of WP is critical for its efficacy, safety, and effectiveness in various applications.
  • Efficient and accurate inspection methods are essential for quality control of WP.

Purpose of the Study:

  • To develop an efficient algorithm for defect detection and physical dimension measurement of Paeoniae Radix Alba decoction pieces.
  • To improve upon existing models like YOLOv8 in terms of accuracy and computational efficiency.
  • To ensure the quality and safety of WP for its dual use in food and medicine.

Main Methods:

  • A novel detection algorithm incorporating a self-developed LSD-Head and Feature Fusion Attention (FCA) mechanism was employed for defect detection.
  • OpenCV technology was utilized for precise measurement of physical dimensions (area, maximum/minimum diameters) of WP slices.
  • The proposed model's performance was benchmarked against the YOLOv8 model.

Main Results:

  • The proposed algorithm demonstrated superior defect detection capabilities compared to YOLOv8.
  • The model achieved a 2.6% accuracy improvement over YOLOv8 while reducing parameter size by 13% and computational load to 65.8% of YOLOv8's.
  • Physical dimension measurements showed an average error of less than 5% compared to actual sizes.

Conclusions:

  • The developed algorithm effectively detects defects and accurately measures physical dimensions of Paeoniae Radix Alba decoction pieces.
  • This method offers a more efficient and accurate quality inspection solution for WP.
  • The findings support the reliable application of WP in food products and health supplements.