Laser & Optoelectronics Progress, Volume. 60, Issue 4, 0410004(2023)
Fine-Grained Classification of Wild Mushrooms Based on Feature Fusion and Attention Mechanism
Fig. 1. Structure diagram of residual block
Fig. 2. Structure diagram of improved ResNet50 and shortcut
Fig. 3. Structure diagram of CBAM module
Fig. 4. Structure diagram of PA_CBAM module
Fig. 5. Training process and residual block+ PA_CBAM
Fig. 6. Residual block+ PA_CBAM
Fig. 7. Image examples from wild mushrooms dataset
Fig. 8. Experimental process of different models on the validation set. (a) Accuracy convergence curve; (b) loss convergence curve
Fig. 9. Experimental process of the comparison experiment on the validation set. (a) Accuracy convergence curve; (b) loss convergence curve
Fig. 10. Diagram of thermodynamic effect comparison
Fig. 11. Experimental process of MobileNet_v2 combined with PA_CBAM on the validation set. (a) Accuracy convergence curve; (b) loss convergence curve
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Jiaxin Qian, Pengfei Yu, Haiyan Li, Hongsong Li. Fine-Grained Classification of Wild Mushrooms Based on Feature Fusion and Attention Mechanism[J]. Laser & Optoelectronics Progress, 2023, 60(4): 0410004
Category: Image Processing
Received: Oct. 21, 2021
Accepted: Dec. 21, 2021
Published Online: Feb. 14, 2023
The Author Email: Yu Pengfei (pfyu@ynu.edu.cn)