Wild Mushroom Classification Based on Improved MobileViT Deep Learning

被引:4
作者
Peng, Youju [1 ]
Xu, Yang [1 ,2 ]
Shi, Jin [1 ]
Jiang, Shiyi [1 ]
机构
[1] Guizhou Univ, Coll Big Data & Informat Engn, Guiyang 550025, Peoples R China
[2] Guiyang Aluminum & Magnesium Design & Res Inst Co, Guiyang 550009, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 08期
关键词
attention mechanism; fine-grained; feature fusion; MobileViT;
D O I
10.3390/app13084680
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Wild mushrooms are not only tasty but also rich in nutritional value, but it is difficult for non-specialists to distinguish poisonous wild mushrooms accurately. Given the frequent occurrence of wild mushroom poisoning, we propose a new multidimensional feature fusion attention network (M-ViT) combining convolutional networks (ConvNets) and attention networks to compensate for the deficiency of pure ConvNets and pure attention networks. First, we introduced an attention mechanism Squeeze and Excitation (SE) module in the MobilenetV2 (MV2) structure of the network to enhance the representation of picture channels. Then, we designed a Multidimension Attention module (MDA) to guide the network to thoroughly learn and utilize local and global features through short connections. Moreover, using the Atrous Spatial Pyramid Pooling (ASPP) module to obtain longer distance relations, we fused the model features from different layers, and used the obtained joint features for wild mushroom classification. We validated the model on two datasets, mushroom and MO106, and the results showed that M-ViT performed the best on the two test datasets, with accurate dimensions of 96.21% and 91.83%, respectively. We compared the performance of our method with that of more advanced ConvNets and attention networks (Transformer), and our method achieved good results.
引用
收藏
页数:18
相关论文
共 50 条
[31]   Improved Deep Learning for Parkinson's Diagnosis Based on Wearable Sensors [J].
Yu, Jintao ;
Meng, Ke ;
Liang, Tingwei ;
Liu, He ;
Wang, Xiaowen .
ELECTRONICS, 2024, 13 (23)
[32]   Distributed deep learning-based signal classification for time-frequency synchronization in wireless networks [J].
Zhang, Qin ;
Guan, Yutong ;
Li, Hai ;
Xiong, Kanghua ;
Song, Zhengyu .
COMPUTER COMMUNICATIONS, 2023, 201 :37-47
[33]   Feature Fusion Based Deep Transfer Learning Based Human Gait Classification Model [J].
Anupama, C. S. S. ;
Zakieva, Rafina ;
Sergin, Afanasiy ;
Lydia, E. Laxmi ;
Kadry, Seifedine ;
Kim, Chomyong ;
Nam, Yunyoung .
INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2023, 37 (02) :1453-1468
[34]   RAD-UNet: Research on an improved lung nodule semantic segmentation algorithm based on deep learning [J].
Wu, Zezhi ;
Li, Xiaoshu ;
Zuo, Jianhui .
FRONTIERS IN ONCOLOGY, 2023, 13
[35]   Frequency learning attention networks based on deep learning for automatic modulation classification in wireless communication [J].
Zhang, Duona ;
Lu, Yuanyao ;
Li, Yundong ;
Ding, Wenrui ;
Zhang, Baochang ;
Xiao, Jing .
PATTERN RECOGNITION, 2023, 137
[36]   Design and Research of Composite Web Page Classification Network Based on Deep Learning [J].
Zhao, Qiuhan ;
Yang, Wenchuan ;
Hua, Rui .
2019 IEEE 31ST INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2019), 2019, :1531-1535
[37]   Deep Learning of Automatic Encoder Based on Attention for ADHD Classification of Brain MRI [J].
Chen, Nan ;
Jiao, Yun .
2023 7TH INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING AND APPLICATIONS, ICBEA, 2023, :11-14
[38]   Deep learning-based multidimensional feature fusion for classification of ECG arrhythmia [J].
Jianfeng Cui ;
Lixin Wang ;
Xiangmin He ;
Victor Hugo C. De Albuquerque ;
Salman A. AlQahtani ;
Mohammad Mehedi Hassan .
Neural Computing and Applications, 2023, 35 :16073-16087
[39]   Deep learning-based detection and condition classification of bridge elastomeric bearings [J].
Liang, Dong ;
Zhang, Shaojie ;
Huang, Hai-Bin ;
Zhang, Luomeng ;
Hu, Yaozong .
AUTOMATION IN CONSTRUCTION, 2024, 166
[40]   Mobile Service Traffic Classification Based on Joint Deep Learning With Attention Mechanism [J].
Li, Changbing ;
Dong, Chao ;
Niu, Kai ;
Zhang, Zhengzhen .
IEEE ACCESS, 2021, 9 :74729-74738