Accurate Segmentation of Tilapia Fish Body Parts Based on Deeplabv3+ for Advancing Phenotyping Applications
Abstract
:1. Introduction
- For the first time, a tilapia part image sample dataset was established, which can be used for accurate part segmentation of tilapia and can provide an effective basis for phenotypic measurement of tilapia.
- According to the morphological characteristics of tilapia, the Deeplabv3+ network structure is improved accordingly to increase the segmentation accuracy of the network as a whole.
- Great improvements in the issues of unclear segmentation of tilapia boundary regions, mis-segmentation of small objects in the presence of overlapping fish, and segmentation errors in complex backgrounds on the Deeplabv3+ tilapia part segmentation dataset.
2. Materials and Methods
2.1. Data Acquisition
2.2. Research Program
2.2.1. Phenotype Classification-Oriented Segmentation Scheme
2.2.2. Experimental Process
2.3. Improved Deeplabv3+ Network Architecture
2.3.1. CBAM Module
2.3.2. SENet Module
3. Experiment and Analysis
3.1. Experimental Environment
3.2. Evaluation Metrics
3.3. Experimental Parameter Setting
3.4. Network Training Results
3.5. Comparative Analysis of Splitting Performance
3.6. Ablation Experiments
4. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Model | IoU (%) | ||||
---|---|---|---|---|---|
Background | Head | Fin | Trunk | Tail | |
PSPNet | 98.14 | 68.89 | 49.26 | 79.19 | 74.45 |
U-Net | 98.90 | 80.67 | 84.70 | 65.63 | 83.65 |
HRNet | 98.98 | 81.04 | 65.27 | 84.12 | 82.77 |
LR-ASPP | 98.82 | 84.00 | 69.37 | 86.39 | 83.39 |
Deeplabv3+ | 99.18 | 79.05 | 75.70 | 83.59 | 83.48 |
Our method | 99.09 | 88.83 | 77.97 | 89.66 | 90.06 |
Model | mIoU (%) | mPA (%) | mRecall (%) |
---|---|---|---|
PSPNet | 73.99 | 83.88 | 84.60 |
U-Net | 82.71 | 90.31 | 89.68 |
HRNet | 82.44 | 89.83 | 89.34 |
LR-ASPP | 84.39 | 91.23 | 91.22 |
Deeplabv3+ | 88.46 | 93.98 | 89.67 |
Our method | 91.69 | 95.94 | 94.21 |
Model | mIoU (%) | mPA (%) |
---|---|---|
Deeplabv3+ + ResNet-50 | 67.54 | 84.11 |
Deeplabv3+ + MobileNetv3 | 72.26 | 86.07 |
Deeplabv3+ + Swin Transformer | 75.71 | 84.99 |
Deeplabv3+ + Xception | 88.46 | 93.98 |
Deeplabv3+ + Xception+ CBAM+ SENet | 91.69 | 95.94 |
Model | SENet | CBAM | mIoU (%) | mPA (%) |
---|---|---|---|---|
Deeplabv3+ | 88.46 | 93.98 | ||
✓ | 89.08 | 94.21 | ||
✓ | 89.11 | 94.08 | ||
✓ | ✓ | 91.69 | 95.94 |
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Share and Cite
Feng, G.; Wang, H.; Chen, M.; Liu, Z. Accurate Segmentation of Tilapia Fish Body Parts Based on Deeplabv3+ for Advancing Phenotyping Applications. Appl. Sci. 2023, 13, 9635. https://doi.org/10.3390/app13179635
Feng G, Wang H, Chen M, Liu Z. Accurate Segmentation of Tilapia Fish Body Parts Based on Deeplabv3+ for Advancing Phenotyping Applications. Applied Sciences. 2023; 13(17):9635. https://doi.org/10.3390/app13179635
Chicago/Turabian StyleFeng, Guofu, Hao Wang, Ming Chen, and Zhixiang Liu. 2023. "Accurate Segmentation of Tilapia Fish Body Parts Based on Deeplabv3+ for Advancing Phenotyping Applications" Applied Sciences 13, no. 17: 9635. https://doi.org/10.3390/app13179635
APA StyleFeng, G., Wang, H., Chen, M., & Liu, Z. (2023). Accurate Segmentation of Tilapia Fish Body Parts Based on Deeplabv3+ for Advancing Phenotyping Applications. Applied Sciences, 13(17), 9635. https://doi.org/10.3390/app13179635