Exploring YOLOv8 Pretrain for Real-Time Detection of Indonesian Native Fish Species





Detection Fish Head and Tail, Deep Learning, Pre-Trained, YOLO Architecture, Ultralytics


The main objective of this research is to determine the efficacy of the YOLO model in detecting native fish species found in Indonesia. Indonesia has a variety of maritime natural resources and shows significant diversity. This research utilizes the YOLO architecture, previously trained on several datasets, for fish detection in the environment in Indonesian waters. This dataset consists of various fish species native to Indonesia and was used to retrain the YOLO Pretrain model. The model was evaluated using test data that accurately represents Indonesian water conditions. Empirical findings show that the modified YOLO Pretrain model can accurately recognize these fish in real-time. After utilizing YOLO and Pre-Train with Ultralytics YOLO Version 8.0.196, the results show an accuracy of 92.3% for head detection, 86.9% for tail detection, and an overall detection accuracy of 89.6%. The fish image dataset, consisting of a total of 401 images, is categorized into three subsets: the training dataset, which consists of 255 images; the validation dataset, which includes 66 images; and the testing dataset, which contains 80 images. This research has great potential for application in fisheries monitoring, marine biology research, and marine environmental monitoring. A real-time fish detection system for the Identification and tracking of fish species is carried out by researchers and field workers. The findings of this research provide a valuable contribution to ongoing efforts aimed at conserving marine biodiversity and implementing more sustainable management practices in Indonesia.

GS Cited Analysis


Download data is not yet available.


Jubayer, F., Soeb, J. A., Mojumder, A. N., Paul, M. K., Barua, P., Kayshar, S., Akter, S. S., Rahman, M., & Islam, A. (2021). Detection of mold on the food surface using YOLOv5. Current Research in Food Science, 4, 724–728. https://doi.org/10.1016/j.crfs.2021.10.003

Kumar, A., Kalia, A., Verma, K., Sharma, A., & Kaushal, M. (2021). Scaling up face masks detection with YOLO on a novel dataset. Optik, 239(March), 166744. https://doi.org/10.1016/j.ijleo.2021.166744

Muksit, A. Al, Hasan, F., Hasan Bhuiyan Emon, M. F., Haque, M. R., Anwary, A. R., & Shatabda, S. (2022). YOLO-Fish: A robust fish detection model to detect fish in realistic underwater environment. Ecological Informatics, 72(October), 101847. https://doi.org/10.1016/j.ecoinf.2022.101847

Oreski, G. (2023). YOLO*C — Adding context improves YOLO performance. Neurocomputing, 555(April). https://doi.org/10.1016/j.neucom.2023.126655

Pacal, I., Karaman, A., Karaboga, D., Akay, B., Basturk, A., Nalbantoglu, U., & Coskun, S. (2022). An efficient real-time colonic polyp detection with YOLO algorithms trained by using negative samples and large datasets. Computers in Biology and Medicine, 141(September 2021). https://doi.org/10.1016/j.compbiomed.2021.105031

Pun, T. B., Neupane, A., Koech, R., & Walsh, K. (2023). Detection and counting of root-knot nematodes using YOLO models with mosaic augmentation. Biosensors and Bioelectronics: X, 15(July). https://doi.org/10.1016/j.biosx.2023.100407

Qiu, Q., & Lau, D. (2023). Real-time detection of cracks in tiled sidewalks using YOLO-based method applied to unmanned aerial vehicle (UAV) images. Automation in Construction, 147(May 2022). https://doi.org/10.1016/j.autcon.2023.104745

Shandilya, S. K., Srivastav, A., Yemets, K., Datta, A., & Nagar, A. K. (2023). YOLO-based segmented dataset for drone vs. bird detection for deep and machine learning algorithms. Data in Brief, 50, 109355. https://doi.org/10.1016/j.dib.2023.109355

Sze, E., Santoso, H., & Hindarto, D. (2022). Review Star Hotels Using Convolutional Neural Network. 7(1), 2469–2477.

Tian, Y., Wang, S., Li, E., Yang, G., Liang, Z., & Tan, M. (2023). MD-YOLO : Multi-scale Dense YOLO for small target pest detection. 213(July).

Xu, L., Dong, S., Wei, H., Ren, Q., Huang, J., & Liu, I. (2023). Defect signal intelligent recognition of weld radiographs based on YOLO V5-IMPROVEMENT. Journal of Manufacturing Processes. www.elsevier.com/locate/manpro

Yu, F., Zhang, G., Zhao, F., Wang, X., Liu, H., Lin, P., & Chen, Y. (2023a). Improved YOLO-v5 model for boosting face mask recognition accuracy on heterogeneous IoT computing platforms. Internet of Things (Netherlands), 23(July). https://doi.org/10.1016/j.iot.2023.100881

Yu, F., Zhang, G., Zhao, F., Wang, X., Liu, H., Lin, P., & Chen, Y. (2023b). Internet of Things Improved YOLO-v5 model for boosting face mask recognition accuracy on heterogeneous IoT computing platforms. 23(July).

Zendehdel, N., Chen, H., & Leu, M. C. (2023). Real-time tool detection in smart manufacturing using You-Only-Look-Once (YOLO)v5. Manufacturing Letters, 35, 1052–1059. https://doi.org/10.1016/j.mfglet.2023.08.062

Zhao, Z., Zheng, T., Hao, K., Xu, J., Cui, S., Liu, X., Zhao, G., Zhou, J., & He, C. (2023). Signal Processing : Image Communication YOLO-PAI : Real-time handheld call behavior detection algorithm and embedded application. Signal Processing: Image Communication, September, 117053. https://doi.org/10.1016/j.image.2023.117053


Crossmark Updates

How to Cite

Hindarto, D. (2023). Exploring YOLOv8 Pretrain for Real-Time Detection of Indonesian Native Fish Species. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(4), 2776-2785. https://doi.org/10.33395/sinkron.v8i4.13100

Most read articles by the same author(s)

1 2 3 > >>