包装与食品机械2024，Vol.42Issue(3)：68-75,8.DOI:10.3969/j.issn.1005-1295.2024.03.010
基于改进U-Net++的燕窝杂质检测方法
Impurity detection in bird's nest based on improved U-Net++
摘要
Abstract
In order to meet the requirements of automated impurity detection in the field of bird's nest to achieve rapid and accurate segmentation of down impurities in bird's nest.A two-stage impurity detection algorithm applied in the field of bird's nest was proposed.The first stage introduces an attention gate(AG)based on the U-Net++model to suppress interference noise caused by inaccurate image segmentation and dense convolution due to uneven bird's nest strength.In the second stage,the probability tensor of feature extraction output is used to achieve precise classification of bird's nest impurity and non-impurity regions through binary masks.bird's nest images are collected and preprocessed,and the test set data of impurity detection algorithms are analyzed and compared with U-Net,U-Net++and traditional image methods under the same conditions through ablation experiments.The experiment shows that the F1 coefficient of the impurity detection algorithm is 94.80%,which is 2.78%,1.12%,20.71%higher than the three algorithms,with a recall rate of 97.90%and an accuracy rate of 91.89%.The overall detection results are better than the comparison algorithms.The study provides a new approach for impurity detection in bird's nest.关键词
图像处理/U-Net++/AG/杂质检测/机器视觉Key words
image processing/U-Net++/AG/impurity detection/machine vision分类
轻工纺织引用本文复制引用
韦龙星,宁萌,蔡礼扬,凤鹏锦,陈义亮..基于改进U-Net++的燕窝杂质检测方法[J].包装与食品机械,2024,42(3):68-75,8.基金项目
国家重点研发计划项目(2022YFD2100304) （2022YFD2100304）
国家自然科学基金项目(51705201) （51705201）
