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基于YOLO-HDR的小龙虾缺陷品质检测方法

王淑青 陈开元 周淼 范博淦

食品与机械2025,Vol.41Issue(3):100-107,8.
食品与机械2025,Vol.41Issue(3):100-107,8.DOI:10.13652/j.spjx.1003.5788.2024.80598

基于YOLO-HDR的小龙虾缺陷品质检测方法

A method for detecting quality defects of crayfish based on YOLO-HDR

王淑青 1陈开元 1周淼 1范博淦1

作者信息

  • 1. 湖北工业大学电气与电子工程学院,湖北 武汉 430068
  • 折叠

摘要

Abstract

[Objective]This study aims to address the problems of single methods,low efficiency,and high costs of the quality inspection of crayfish in industrial processing.[Methods]A YOLO-HDR-based lightweight neural network model was proposed.The PP-HGNetv2 model was employed to design a new YOLOv8 backbone network,and the lightweight modules of HGstem and DWConv were introduced to reconstruct the network.The dynamic convolution block and other lightweight convolutions(GhostConv and RepConv)in the official library were used to redesign the HGBlock of the new backbone network.The dynamic high-performance network modules(DynamicHGBlock,RepHGBlock,and GhostHGBlock)were obtained to improve HGBlock and the feature expression of the network.The C2f module of the original neck network was improved by the repeated cross-stage local edge-preserving attention network RepNCSPELAN4 to address the performance degradation caused by the lightweight network.[Results]The accuracy and average precision of the improved model reached 92.8%and 95.9%,respectively,which were 3.5%and 1.9%higher than those of the original model and better than those of other comparative target detection algorithms.The number of parameters and model size of the improved model were reduced by 17.7%and 16.2%,respectively,compared with those of the original YOLOv8n model,and the amount of computation was reduced by 19.8%.[Conclusion]The method established in this study demonstrates improved detection performance under the dense occlusion noise background,enabling the quality inspection of crayfish in industrial processing in the complex background before frozen packaging.

关键词

深度学习/品质检测/小龙虾/注意力机制/PP-HGNetV2

Key words

deep learning/quality inspection/crayfish/attention mechanism/PP-HGNetV2

引用本文复制引用

王淑青,陈开元,周淼,范博淦..基于YOLO-HDR的小龙虾缺陷品质检测方法[J].食品与机械,2025,41(3):100-107,8.

基金项目

国家自然科学基金(编号:62306107) (编号:62306107)

食品与机械

OA北大核心

1003-5788

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