Ship Segmentation via Combined Attention Mechanism and Efficient Channel Attention High-Resolution Representation Network
Abstract
:1. Introduction
2. Methodology
2.1. Basic Ship Segmentation Structure
2.2. CA2HRNET Framework
2.2.1. Channel Attention Mechanism
2.2.2. Details about CA2HRNET Framework
3. Experimental Section
3.1. Experimental Data
3.2. Experimental Setups
3.3. Evaluation Index
3.4. Results and Analysis
3.4.1. Scenario 1 Experimental Results
3.4.2. Experimental Results in Scenario 2
3.4.3. Experiment Results in Scenario 3
4. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Video Number | Frame Rate | Image Resolution | Video Duration | Environmental Feature |
---|---|---|---|---|
scenario 1 | 30 | 1920 × 1080 | 15 | slightly cloudy, fewer ships, no waves, low visibility |
scenario 2 | 30 | 1920 × 1080 | 18 | slightly cloudy, fewer ships, no waves, high visibility |
scenario 3 | 30 | 1920 × 1080 | 9 | cloudy, many ships, waves, high visibility |
MODEL | |||||
---|---|---|---|---|---|
CA2HRNET | 99.81% | 97.81% | 97.56% | 97.53% | 99.63% |
HRNet-SE | 99.81% | 97.79% | 97.53% | 97.49% | 99.63% |
HRNet-CA | 99.81% | 97.53% | 97.52% | 97.50% | 99.62% |
HRNet | 99.67% | 95.31% | 95.78% | 95.78% | 99.36% |
PSPNet | 99.41% | 93.03% | 92.34% | 92.58% | 98.87% |
UNet | 99.77% | 98.47% | 96.97% | 96.94% | 99.54% |
DeepLabv3+ | 99.68% | 94.22% | 95.89% | 95.88% | 99.37% |
MODEL | Accuracy | Precision | F1-Score | mIoU | FWIoU |
---|---|---|---|---|---|
CA2HRNET | 99.80% | 97.14% | 96.59% | 96.60% | 99.61% |
HRNet-SE | 99.80% | 97.00% | 96.55% | 96.57% | 99.61% |
HRNet-CA | 99.80% | 96.95% | 96.53% | 96.55% | 99.60% |
HRNet | 99.63% | 92.65% | 93.61% | 93.80% | 99.28% |
PSPNet | 99.57% | 93.88% | 92.45% | 92.76% | 99.16% |
UNet | 99.76% | 97.00% | 95.74% | 95.79% | 99.52% |
DeepLabv3+ | 99.65% | 95.45% | 94.00% | 94.16% | 99.33% |
MODEL | Accuracy | Precision | F1-Score | mIoU | FWIoU |
---|---|---|---|---|---|
CA2HRNET | 99.69% | 97.69% | 96.85% | 96.79% | 99.40% |
HRNet-SE | 99.68% | 97.69% | 96.72% | 96.66% | 99.37% |
HRNet-CA | 99.68% | 97.60% | 96.76% | 96.70% | 99.38% |
HRNet | 99.43% | 93.58% | 94.26% | 94.27% | 98.91% |
PSPNet | 99.33% | 93.73% | 93.06% | 93.16% | 98.70% |
UNet | 99.66% | 97.36% | 96.51% | 96.45% | 99.33% |
DeepLabv3+ | 99.42% | 93.43% | 94.14% | 94.16% | 98.88% |
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Li, X. Ship Segmentation via Combined Attention Mechanism and Efficient Channel Attention High-Resolution Representation Network. J. Mar. Sci. Eng. 2024, 12, 1411. https://doi.org/10.3390/jmse12081411
Li X. Ship Segmentation via Combined Attention Mechanism and Efficient Channel Attention High-Resolution Representation Network. Journal of Marine Science and Engineering. 2024; 12(8):1411. https://doi.org/10.3390/jmse12081411
Chicago/Turabian StyleLi, Xiaoyi. 2024. "Ship Segmentation via Combined Attention Mechanism and Efficient Channel Attention High-Resolution Representation Network" Journal of Marine Science and Engineering 12, no. 8: 1411. https://doi.org/10.3390/jmse12081411