Jujube Variety Recognition Based on Improved Attention Mechanism and Multi-semantic Feature Enhancement

被引：0
|
作者：
Lei, Hao [1 ]
Yuan, Yingchun [1 ]
Xu, Nan [1 ]
He, Zhenxue [1 ]
机构：
[1] College of Information Science and Technology, Hebei Agricultural University, Baoding
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2024年 / 55卷 / 07期
关键词：
Attention mechanism; Deep learning; Jujube variety recognition; Multi-semantic feature enhancement;
D O I：
10.6041/j.issn.1000-1298.2024.07.026
中图分类号：
学科分类号：
摘要：
In response to the low accuracy of jujube variety recognition in current natural scenarios, a jujube variety recognition model was proposed based on attention mechanism and multi-semantic feature enhancement (ICBAM_MSFE_Res50 ) . On the basis of ResNet - 50 , the attention mechanism ICBAM (improved convolutional block attention module) was introduced. ICBAM improved the convolutional block attention module (CBAM) by using one-dimensional convolution and multi-scale hole convolution, eliminating information loss during feature map dimensionality reduction, reducing the computational and parameter complexity of the model, and improving the model's ability to extract fine-grained features in jujube fruit regions. At the same time, a multi-semantic feature enhancement ( MSFE ) module was proposed, which extracted more local salient features of jujube fruit through jujube fruit region localization algorithm, and used saliency feature suppression algorithm to force the model to learn secondary features of jujube fruit, thereby achieving the learning of multiple semantic features of jujube fruit. The experimental results showed that the accuracy of the model on the dataset of 20 types of jujube varieties was 92. 20% , which was 4. 26 percentage points higher than that of ResNet - 50. Compared with the AlexNet, VGG - 16, ResNet - 18, and InceptionV3 models, the accuracy was improved by 15. 84, 9.22, 6.86, and 3.55 percentage points, respectively. Compared with other jujube variety recognition methods, this method still performed the best in the recognition of 20 types of jujube, which can provide reference for research on jujube variety recognition in natural scenarios. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.
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页码：270 / 279and324
相关论文
共 31 条
  • [1] Mengjun LIU, WANG Jiurui, Fruit scientific research in New China in the past 70 years: Chinese jujube [J], Journal of Fruit Science, 36, 10, pp. 1369-1381, (2019)
  • [2] LECUN Y, BENGIO Y, HINTON G., Deep learning[ J], Nature, 521, 7553, pp. 436-444, (2015)
  • [3] KRIZHEVSKY A, SUTSKEVER I, HINTON G E., ImageNet classification with deep convolutional neural networks [ J], Commun. ACM, 60, 6, pp. 84-90, (2017)
  • [4] SIMONY AN K, ZISSERMA A., Very deep convolutional networks for large-scale image recognition [ J], (2014)
  • [5] HE K, ZHANG X, REN S, Et al., Deep residual learning for image recognition [ C], Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, (2016)
  • [6] YU Youjiang, YU Caili, SHANG Yuanhang, Et al., Classification and recognition of jujube varieties based on Stacking model fusion [ J], Northern Horticulture, 8, pp. 139-148, (2022)
  • [7] MENG X, YUAN Y C, TENG G F, Et al., Deep learning for fine-grained classification of jujube fruit in the natural environment [ J], Journal of Food Measurement and Characterization, 15, 5, pp. 4150-4165, (2021)
  • [8] ALBARRAK K, GULZAR Y, HAMID Y, Et al., A deep learning-based model for date fruit classification [ J], Sustainability, 14, 10, (2022)
  • [9] GENG L, XU W, ZHANG F, Et al., Dried jujube classification based on a double branch deep fusion convolution neural network [ J], Food Science and Technology Research, 24, 6, pp. 1007-1015, (2018)
  • [10] MENG Xi, YUAN Yingchun, LIU Bo, Et al., Classification of jujube by integrating fruit with leaf information [ J], Journal of Hebei Agricultural University, 45, 2, pp. 107-113, (2022)
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