基于改进U-Net++的燕窝杂质检测方法OA北大核心CSTPCD

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.