Instance Segmentation of Shrimp Based on Contrastive Learning
Source abstract
Abstract
Shrimp farming has traditionally served as a crucial source of seafood and revenue for coastal countries. However, with the rapid development of society, conventional small-scale manual shrimp farming can no longer meet the increasing demand for rapid growth. As a result, it is imperative to continuously develop automation technology for efficient large-scale shrimp farming. Smart shrimp farming represents an innovative application of advanced technologies and management practices in shrimp aquaculture to expand the scale of production. Nonetheless, the use of these new technologies is not without difficulties, including the scarcity of public datasets and the high cost of labeling. In this paper, we focus on the application of advanced computer vision techniques to shrimp farming. To achieve this objective, we first establish a high-quality shrimp dataset for training various deep learning models. Subsequently, we propose a method that combines unsupervised learning with downstream instance segmentation tasks to mitigate reliance on large training datasets. Our experiments demonstrate that the method involving contrastive learning outperforms the direct fine-tuning of an instance segmentation model for shrimp in instance segmentation tasks. Furthermore, the concepts presented in this paper can extend to other fields that utilize computer vision technologies.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Water Quality Monitoring Technologies · Aquatic life and conservation · Oil Palm Production and Sustainability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Division of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju 54896, Republic of Korea
Division of Aquatic Life Culturing, Korea National University of Agriculture and Fisheries, Jeonju 54874, Republic of Korea
Daesang Aquaculture Trout Association Corporation, Taean 32158, Republic of Korea
Funding
Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries
References (33)
Show source-provided references
1. IoT Based Integrated System to Monitor the Ideal Environment for Shrimp Cultivation with Android Mobile Application
2. Armalivia, S., Zainuddin, Z., Achmad, A., and Wicaksono, M.A. (2021, January 28–30). Automatic Counting Shrimp Larvae Based You Only Look Once (YOLO). Proceedings of the 2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS), Bandung, Indonesia.10.1109/AIMS52415.2021.9466058 ↗
3. Vaname (Litopenaeus vannamei) shrimp fry counting based on image processing method10.1088/1755-1315/147/1/012014 ↗
4. Thai, T.T.N., Nguyen, T.S., and Pham, V.C. (2021, January 15–16). Computer vision based estimation of shrimp population density and size. Proceedings of the 2021 International symposium on electrical and electronics engineering (ISEE), Ho Chi Minh, Vietnam.10.1109/ISEE51682.2021.9418638 ↗
5. Yu, H., Liu, X., Qin, H., Yang, L., and Chen, Y. (2022, January 18–21). Automatic Detection of Peeled Shrimp Based on Image Enhancement and Convolutional Neural Networks. Proceedings of the 8th International Conference on Computing and Artificial Intelligence, Tianjin, China.10.1145/3532213.3532279 ↗
6. Automatic shrimp counting method using local images and lightweight YOLOv410.1016/j.biosystemseng.2022.05.011 ↗
7. Dimensionality reduction by learning an invariant mapping10.1109/CVPR.2006.100 ↗
8. He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020, January 13–19). Momentum contrast for unsupervised visual representation learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.00975 ↗
9. Chen, X., Fan, H., Girshick, R., and He, K. (2020). Improved baselines with momentum contrastive learning. arXiv.
10. Chen, X., Xie, S., and He, K. (2021, January 11–17). An empirical study of training self-supervised vision transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada.10.1109/ICCV48922.2021.00950 ↗
11. Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020, January 13–18). A simple framework for contrastive learning of visual representations. Proceedings of the International Conference on Machine Learning, Virtual.
12. Bootstrap your own latent-a new approach to self-supervised learning
13. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
14. He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017, January 22–29). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.322 ↗
15. Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C.L. (2014, January 6–12). Microsoft coco: Common objects in context. Proceedings of the Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland. Proceedings, Part V 13.10.1007/978-3-319-10602-1_48 ↗
16. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
17. Nguyen, K.T., Nguyen, C.N., Wang, C.Y., and Wang, J.C. (2020, January 4–6). Two-phase instance segmentation for whiteleg shrimp larvae counting. Proceedings of the 2020 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.10.1109/ICCE46568.2020.9043075 ↗
18. Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv.
19. Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (November, January 27). Searching for mobilenetv3. Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea.
20. Hu, W.C., Wu, H.T., Zhang, Y.F., Zhang, S.H., and Lo, C.H. (2020). Shrimp recognition using ShrimpNet based on convolutional neural network. J. Ambient. Intell. Humaniz. Comput., 1–8.10.1007/s12652-020-01727-3 ↗
21. Study of shrimp recognition methods using smart networks10.1016/j.compag.2019.104926 ↗
22. Soft-shell shrimp recognition based on an improved AlexNet for quality evaluations10.1016/j.jfoodeng.2019.109698 ↗
23. Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5–9). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015: 18th International Conference, Munich, Germany. Proceedings, Part III 18.
24. Mutual contrastive learning for visual representation learning
25. Ge, S., Mishra, S., Kornblith, S., Li, C.L., and Jacobs, D. (2022). Hyperbolic Contrastive Learning for Visual Representations beyond Objects. arXiv.
26. Wu, Y., Wang, Z., Zeng, D., Li, M., Shi, Y., and Hu, J. (2022, January 23–29). Decentralized unsupervised learning of visual representations. Proceedings of the Proceedings of the Thirty-First, International Joint Conference on Artificial Intelligence, IJCAI, Vienna, Austria.10.24963/ijcai.2022/323 ↗
27. Cole, E., Yang, X., Wilber, K., Mac Aodha, O., and Belongie, S. (2022, January 18–24). When does contrastive visual representation learning work?. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.10.1109/CVPR52688.2022.01434 ↗
28. Yu, E., Li, Z., and Han, S. (2022, January 18–24). Towards Discriminative Representation: Multi-View Trajectory Contrastive Learning for Online Multi-Object Tracking. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.10.1109/CVPR52688.2022.00863 ↗
29. Wang, P., Han, K., Wei, X.S., Zhang, L., and Wang, L. (2021, January 20–25). Contrastive Learning Based Hybrid Networks for Long-Tailed Image Classification. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.10.1109/CVPR46437.2021.00100 ↗
30. Hyperspectral Imagery Classification Based on Contrastive Learning10.1109/TGRS.2022.3215431 ↗
31. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20–25). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.10.1109/CVPR.2009.5206848 ↗
32. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv.
33. Contributors, M. (2023, January 17). MMSelfSup: OpenMMLab Self-Supervised Learning Toolbox and Benchmark. 2021. Available online: https://github.com/open-mmlab/mmselfsup.