Multi-View and Multi-Type Feature Fusion Rotor Biofouling Recognition Method for Tidal Stream Turbine
Abstract
:1. Introduction
- A semantic segmentation method based on MVTFF is proposed for TST biofouling recognition, aiming to improve the accuracy of TST biofouling recognition in turbid environments.
- The rotor contour features are extracted by explicit shape priors, which is conducive to more accurate positioning and recognition of targets.
- We enhance semantic information by integrating and interacting different perspectives and different types of features to improve semantic segmentation effects.
2. Problem Description
3. MVTFF-Based Biofouling Recognition for TST Rotor
3.1. Basic Process of MVTFF
3.2. Explicit Shape Prior for Training Process
3.3. Multi-View and Multi-Type Feature Fusion
3.4. Parameter Transfer for Pretrained
4. Results and Discussion
4.1. Image Dataset of TST
4.2. Implement Details of the MVTFF
4.3. Ablation Study on TST Dataset
4.4. Comparison with State-of-the-Art Methods
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
TST | tidal stream turbine |
MVTFF | multi-view and multi-type feature fusion |
mIoU | mean intersection over union |
mPA | mean pixel accuracy |
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Components | Layer Type | Input Size | Parameter |
---|---|---|---|
Backbone Processing | Conv3x3 × 2 | (B, in_dim, H, W) | stride = 1, padding = 1 |
Feature Fusion | Conv3x3 × 2 | (B, 192, H, W) | stride = 1, padding = 1 |
Dataset Attribute | |
---|---|
Image size | 640 × 576 |
Training set | 1100 |
Testing set | 200 |
mIoU | mPA | Precision | Recall | |
---|---|---|---|---|
different views | 68.84 | 83.87 | 79.32 | 83.87 |
single view | 63.53 | 70.34 | 81.15 | 70.34 |
mIoU | mPA | Precision | Recall | |
---|---|---|---|---|
shape priors | 68.84 | 83.87 | 79.32 | 83.87 |
no shape priors | 58.98 | 69.16 | 74.78 | 69.16 |
Method | MVTFF | Unet | Swin-Unet | DeeplabV3+ | SETR |
---|---|---|---|---|---|
mIoU | 68.84 | 54.22 | 66.92 | 59.79 | 40.21 |
mPA | 83.87 | 75.25 | 82.84 | 73.50 | 57.26 |
Precision | 79.32 | 81.36 | 83.90 | 75.50 | 58.79 |
Recall | 83.87 | 75.25 | 82.84 | 73.50 | 57.26 |
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Xu, H.; Yang, D.; Wang, T.; Benbouzid, M. Multi-View and Multi-Type Feature Fusion Rotor Biofouling Recognition Method for Tidal Stream Turbine. J. Mar. Sci. Eng. 2025, 13, 356. https://doi.org/10.3390/jmse13020356
Xu H, Yang D, Wang T, Benbouzid M. Multi-View and Multi-Type Feature Fusion Rotor Biofouling Recognition Method for Tidal Stream Turbine. Journal of Marine Science and Engineering. 2025; 13(2):356. https://doi.org/10.3390/jmse13020356
Chicago/Turabian StyleXu, Haoran, Dingding Yang, Tianzhen Wang, and Mohamed Benbouzid. 2025. "Multi-View and Multi-Type Feature Fusion Rotor Biofouling Recognition Method for Tidal Stream Turbine" Journal of Marine Science and Engineering 13, no. 2: 356. https://doi.org/10.3390/jmse13020356
APA StyleXu, H., Yang, D., Wang, T., & Benbouzid, M. (2025). Multi-View and Multi-Type Feature Fusion Rotor Biofouling Recognition Method for Tidal Stream Turbine. Journal of Marine Science and Engineering, 13(2), 356. https://doi.org/10.3390/jmse13020356