Fine-Grained Analysis of Coral Instance Segmentation using YOLOv8 Models

Authors

  • Wahyu Maulana Hassanudin Universitas Semarang
  • Victor Gayuh Utomo Universitas Semarang
  • Riski Apriyanto Universitas Semarang

DOI:

10.33395/sinkron.v8i2.13583

Keywords:

Automated Monitoring, Coral Reefs, Instance Segmentation, Marine Ecosystems, YOLOv8

Abstract

Within the geographical boundaries of Indonesia, coral reefs flourish as intricate ecosystems bustling with a variety of marine creatures that play a crucial role, in preserving biodiversity. However this delicate harmony faces threats from climate change and human activities, leading to the risk of species loss. Despite growing awareness surrounding these challenges effectively and swiftly monitoring conditions remains a task. Existing methods for assessing corals often fall short due to requiring extensive specialist knowledge, lacking large-scale coverage, and being costly to implement. To tackle these obstacles this research suggests an approach for automated reef monitoring using instance segmentation with a YOLOv8 model. Leveraging YOLOv8 segmentation capabilities enables efficient analysis of corals. A systematic process is employed involving data collection, preparation (including techniques like Histogram Equalization), training the model on a reef dataset, model evaluation and enhancing the segmentation mask. The outcomes reveal the YOLOv8m Pp model with 96.7% precision 95.9% recall rate and a mean Average Precision (mAP50) score of 98.2%. This study demonstrates the potential of YOLOv8 to accurately segment instances for monitoring reefs in Indonesia, hence facilitating improved conservation strategies.

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References

Bello, R. W., Oluigbo, C. U., Peanock, I. G., & Moradeyo, O. M. (2023). Underwater Image Enhancement for Instance Segmentation using Deep Learning Models. Journal of Applied Sciences and Environmental Management, 27(2), 243–247. https://doi.org/10.4314/jasem.v27i2.9

Bolya, D., Fanyi, C. Z., Yong, X., & Lee, J. (2019). YOLACT Real-time Instance Segmentation. IEEE International Conference on Computer Vision, 9157–9166. https://github.com/dbolya/yolact.

Burns, C., Bollard, B., & Narayanan, A. (2022). Machine-Learning for Mapping and Monitoring Shallow Coral Reef Habitats. In Remote Sensing (Vol. 14, Issue 11). MDPI. https://doi.org/10.3390/rs14112666

Candela, A., Edelson, K., Gierach, M. M., Thompson, D. R., Woodward, G., & Wettergreen, D. (2021). Using Remote Sensing and in situ Measurements for Efficient Mapping and Optimal Sampling of Coral Reefs. Frontiers in Marine Science, 8. https://doi.org/10.3389/fmars.2021.689489

Chen, J., Zhu, S., & Luo, W. (2024). Instance Segmentation of Underwater Images by Using Deep Learning. Electronics (Switzerland), 13(2). https://doi.org/10.3390/electronics13020274

Corrigan, B. C., Tay, Z. Y., & Konovessis, D. (2023). Real-Time Instance Segmentation for Detection of Underwater Litter as a Plastic Source. Journal of Marine Science and Engineering, 11(8). https://doi.org/10.3390/jmse11081532

Denley, D., Metaxas, A., & Scheibling, R. (2020). Subregional variation in cover and diversity of hard coral (Scleractinia) in the Western Province, Solomon Islands following an unprecedented global bleaching event. PLoS ONE, 15(11 November). https://doi.org/10.1371/journal.pone.0242153

Dumitriu, A., Tatui, F., Miron, F., Ionescu, R. T., & Timofte, R. (2023). Rip Current Segmentation: A Novel Benchmark and YOLOv8 Baseline Results. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/CVPRW59228.2023.00133

Eddy, T. D., Lam, V. W. Y., Reygondeau, G., Cisneros-Montemayor, A. M., Greer, K., Palomares, M. L. D., Bruno, J. F., Ota, Y., & Cheung, W. W. L. (2021). Global decline in capacity of coral reefs to provide ecosystem services. One Earth, 4(9), 1278–1285. https://doi.org/10.1016/j.oneear.2021.08.016

Hendee, J., Amornthammarong, N., Gramer, L., & Gomez, A. (2020). A novel low-cost, high-precision sea temperature sensor for coral reef monitoring. Bulletin of Marine Science, 96(1), 97–109. https://doi.org/10.5343/bms.2019.0050

Hoadley, K. D., Lewis, A. M., Wham, D. C., Pettay, D. T., Grasso, C., Smith, R., Kemp, D. W., LaJeunesse, T. C., & Warner, M. E. (2019). Host–symbiont combinations dictate the photo-physiological response of reef-building corals to thermal stress. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-46412-4

Inui, A., Mifune, Y., Nishimoto, H., Mukohara, S., Fukuda, S., Kato, T., Furukawa, T., Tanaka, S., Kusunose, M., Takigami, S., Ehara, Y., & Kuroda, R. (2023). Detection of Elbow OCD in the Ultrasound Image by Artificial Intelligence Using YOLOv8. Applied Sciences (Switzerland), 13(13). https://doi.org/10.3390/app13137623

Jiang, P., Ergu, D., Liu, F., Cai, Y., & Ma, B. (2021). A Review of Yolo Algorithm Developments. Procedia Computer Science, 199, 1066–1073. https://doi.org/10.1016/j.procs.2022.01.135

Lai, Y., Zhou, Z., Su, B., Xuanyuan, Z., Tang, J., Yan, J., Liang, W., & Chen, J. (2022). Single underwater image enhancement based on differential attenuation compensation. Frontiers in Marine Science, 9. https://doi.org/10.3389/fmars.2022.1047053

Lara-Pulido, J. A., Mojica, Á., Bruner, A., Guevara-Sanginés, A., Simon, C., Vásquez-Lavin, F., González-Baca, C., & Infanzón, M. J. (2021). A business case for marine protected areas: Economic valuation of the reef attributes of cozumel island. Sustainability (Switzerland), 13(8). https://doi.org/10.3390/su13084307

Lei, X., Wang, H., Shen, J. I. E., Chen, Z. H. E., & Zhang, W. (2021). A novel intelligent underwater image enhancement method via color correction and contrast stretching. Microprocessors and Microsystems, 104040. https://doi.org/https://doi.org/10.1016/j.micpro.2021.104040

Li, Z. (2019). Symbiotic Microbiomes of Coral Reefs Sponges and Corals. In Symbiotic Microbiomes of Coral Reefs Sponges and Corals. Springer Netherlands. https://doi.org/10.1007/978-94-024-1612-1

Lv, C., Cao, S., Zhang, Y., Xu, G., & Zhao, B. (2022). Methods studies for attached marine organisms detecting based on convolutional neural network. Energy Reports, 8, 1192–1201. https://doi.org/10.1016/j.egyr.2022.08.131

Mohan, S., & Simon, P. (2020). Underwater Image Enhancement based on Histogram Manipulation and Multiscale Fusion. Procedia Computer Science, 171, 941–950. https://doi.org/10.1016/j.procs.2020.04.102

Montano, S. (2020). The extraordinary importance of coral-associated fauna. In Diversity (Vol. 12, Issue 9). MDPI AG. https://doi.org/10.3390/D12090357

Peng, Y. T., Chen, Y. R., Chen, Z., Wang, J. H., & Huang, S. C. (2022). Underwater Image Enhancement Based on Histogram-Equalization Approximation Using Physics-Based Dichromatic Modeling. Sensors, 22(6). https://doi.org/10.3390/s22062168

Picek, L., Říha, A., & Zita, A. (2020). Coral Reef annotation, localisation and pixel-wise classification using Mask R-CNN and Bag of Tricks. https://github.com/tensorflow/models/blob/master/research/object_detection

Qin, Z., Tao, J., Tong, F., Zhang, H., Qu, F., & Han, X. (2019). A Fast Proportionate RLS Adaptive Equalization for Underwater Acoustic Communications. OCEANS 2019 - Marseille. https://doi.org/10.1109/OCEANSE.2019.8867338

Quoc Toan, N. (2022). Detrimental Starfish Detection on Embedded System: A Case Study of YOLOv5 Deep Learning Algorithm and TensorFlow Lite framework. Journal of Computer Sciences Institute.

Salau, J., & Krieter, J. (2020). Instance segmentation with mask R-CNN applied to loose-housed dairy cows in a multi-camera setting. Animals, 10(12), 1–19. https://doi.org/10.3390/ani10122402

Steffens, A., Campello, A., Ravenscroft, J., Clark, A., & Hagras, H. (2019). Deep Segmentation: Using deep convolutional networks for coral reef pixel-wise parsing. Conference and Labs of the Evaluation Forum.

Talaat, F. M., & ZainEldin, H. (2023). An improved fire detection approach based on YOLO-v8 for smart cities. Neural Computing and Applications, 35(28), 20939–20954. https://doi.org/10.1007/s00521-023-08809-1

Varma, N. (2024). segmentation_corals Dataset. Roboflow Universe. https://universe.roboflow.com/nuthan-varma-b5j2d/segmentation_corals

Yang, G., Wang, J., Nie, Z., Yang, H., & Yu, S. (2023). A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention. Agronomy, 13(7). https://doi.org/10.3390/agronomy13071824

Zhao, M., Zhang, H., Zhong, Y., Jiang, D., Liu, G., Yan, H., Zhang, H., Guo, P., Li, C., Yang, H., Chen, T., & Wang, R. (2019). The status of coral reefs and its importance for coastal protection: A case study of northeastern Hainan Island, South China Sea. Sustainability (Switzerland), 11(16). https://doi.org/10.3390/su11164354

Zhao, S., Zheng, J., Sun, S., & Zhang, L. (2022). An Improved YOLO Algorithm for Fast and Accurate Underwater Object Detection. Symmetry, 14(8). https://doi.org/10.3390/sym14081669

Zhao, X., Ding, W., An, Y., Du, Y., Yu, T., Li, M., Tang, M., & Wang, J. (2023). Fast Segment Anything. http://arxiv.org/abs/2306.12156

Zhong, J., Li, M., Qin, J., Cui, Y., Yang, K., & Zhang, H. (2022). Real-time marine animal detection using yolo-based deep learning networks in the coral reef ecosystem. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 46(3/W1-2022), 301–306. https://doi.org/10.5194/isprs-archives-XLVI-3-W1-2022-301-2022

Zhong, J., Li Zurich, M., Zhang, H., & Qin, J. (2023). Combining Photogrammetric Computer Vision and Semantic Segmentation for Fine-grained Understanding of Coral Reef Growth under Climate Change. Applications of Computer Vision Workshops (WACVW). https://doi.org/10.1109/WACVW58289.2023.00024

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How to Cite

Hassanudin, W. M. ., Utomo, V. G. ., & Apriyanto, R. (2024). Fine-Grained Analysis of Coral Instance Segmentation using YOLOv8 Models. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(2), 1047-1055. https://doi.org/10.33395/sinkron.v8i2.13583