FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-130310

Research Article |

Real-Time Oil Spill Detection with YOLO Framework for Marine Ecosystem Surveillance

Author(s): Srinivas Talasila1*,Vijaya Kumar Gurrala2, Varshini M3,Siva Sai Balla4, Madhuri M5, Chinthakindi Kiran Kumar6

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 13, Issue 2

Publisher : FOREX Publication

Published : 30 August 2025

e-ISSN : 2347-470X

Page(s) : 463-470

DOI: https://doi.org/10.37391/IJEER.130310

Abstract

Early detection of oil spills is crucial and essential for marine environments to minimize environmental harm and enable quick responsive measures. Oil spills can cause significant ecological and financial losses, which emphasizes the need for an efficient monitoring system. This paper presents the use of YOLO deep learning algorithms to enhance the oil spill detection speed and accuracy. A robust and high-quality dataset is taken, consisting of images extracted from Roboflow. To maximize the data quality, preprocessing techniques such as label normalization, contrast enhancement and noise reduction were used. The proposed YOLO algorithms were trained using Adam and SGDM optimizers with an initial learning rate of 0.01, 0.001 and 0.0001. Among the adopted YOLO models, YOLOv9 yielded impressive results with an mAP@0.5 of 94.45%, precision of 95.6%, recall of 93.3% and F1 Score of 94.44%. The recommended system, which incorporates deep learning technologies into marine environment monitoring, greatly improves the marine surveillance systems for oil spill detection and emergency response capabilities by enabling real-time monitoring.

Keywords: Deep Learning, Image Processing, Oil Spill Detection, Marine Ecosystem, YOLO Algorithms.




Srinivas Talasila,Department of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India; Email: srinivas_t@vnrvjiet.in

Vijaya Kumar Gurrala , Department of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India; Email: vijayakumar_g@vnrvjiet.in

Varshini M, Department of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India;Email: varshinics.m@gmail.com

Siva Sai Balla, Department of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India; Email: saishiva216@gmail.com

Madhuri M, Department of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India; Email: madhurigoudmusthyala@gmail.com

Chinthakindi Kiran Kumar, Department of Electronics and Communication Engineering, Malla Reddy College of Engineering and Technology, Hyderabad, India; Email: ckkmtech11@gmail.com

    [1] Akhmedov, F., Nasimov, R., & Abdusalomov, A. (2024). Developing a comprehensive oil spill detection model for marine environments. Remote Sensing, 16(16), 3080. https://doi.org/10.3390/rs16163080.
    [2] Khalturin, A. A., Parfenchik, K. D., & Shpenst, V. A. (2023b). Features of oil spills monitoring on the water surface by the Russian Federation in the Arctic Region. Journal of Marine Science and Engineering, 11(1), 111. https://doi.org/10.3390/jmse11010111.
    [3] Vasconcelos, R. N., Lima, A. T. C., Lentini, C. a. D., Miranda, J. G. V., De Mendonça, L. F. F., Lopes, J. M., Santana, M. M. M., Cambuí, E. C. B., Souza, D. T. M., Costa, D. P., Duverger, S. G., & Franca-Rocha, W. S. (2023). Deep Learning-Based Approaches for Oil Spill Detection: A Bibliometric review of research trends and challenges. Journal of Marine Science and Engineering, 11(7), 1406. https://doi.org/10.3390/jmse11071406.
    [4] ITOPF. (n.d.). Oil tanker spill Statistics 2023 - ITOPF. https://www.itopf.org/knowledge-resources/data-statistics/statistics.
    [5] V, S., & Saro, V. A. (2024). OSD-DNN: Oil Spill Detection using Deep Neural Networks. International Journal of Performability Engineering, 20(2), 57. https://doi.org/10.23940/ijpe.24.02.p1.5767.
    [6] Gui, S., Song, S., Qin, R., & Tang, Y. (2024). Remote Sensing Object Detection in the Deep Learning Era—A Review. Remote Sensing, 16(2), 327. https://doi.org/10.3390/rs16020327.
    [7] Amani, M., Mehravar, S., Asiyabi, R. M., Moghimi, A., Ghorbanian, A., Ahmadi, S. A., Ebrahimy, H., Moghaddam, S. H. A., Naboureh, A., Ranjgar, B., Mohseni, F., Nazari, M. E., Mahdavi, S., Mirmazloumi, S. M., Ojaghi, S., & Jin, S. (2022). Ocean Remote Sensing Techniques and Applications: A Review (Part II). Water, 14(21), 3401. https://doi.org/10.3390/w14213401.
    [8] Tayná Cristina Sousa Silva, Monik Silva Sousa, & João Viana da Fonseca Neto. (2024). Deep Learning for Oil Spill Detection and Classification in Ocean Surface. International Journal of Advances in Engineering and Technology, 17(1), 13–24. https://doi.org/10.5281/zenodo.10860934.
    [9] Cai, Y., Chen, L., Zhuang, X., & Zhang, B. (2024). Automated marine oil spill detection algorithm based on single-image generative adversarial network and YOLO-v8 under small samples. Marine Pollution Bulletin, 203, 116475. https://doi.org/10.1016/j.marpolbul.2024.116475.
    [10] M. P. Shanmukh, S. B. Priya and T. Madeswaran, "Improving Oil Spill Detection in Marine Environments Through Deep Learning Approaches," 2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India, 2024, pp. 1-6, doi: 10.1109/ICAECT60202.2024.10468888.
    [11] T. Trongtirakul, S. Agaian, A. Oulefki and K. Panetta, "Method for Remote Sensing Oil Spill Applications Over Thermal and Polarimetric Imagery," in IEEE Journal of Oceanic Engineering, vol. 48, no. 3, pp. 973-987, July 2023, doi: 10.1109/JOE.2023.3245759.
    [12] Patel, K., Bhatt, C., Corchado, J.M. (2023). Automatic Detection of Oil Spills from SAR Images Using Deep Learning. In: Julián, V., Carneiro, J., Alonso, R.S., Chamoso, P., Novais, P. (eds) Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence. ISAmI 2022. Lecture Notes in Networks and Systems, vol 603. Springer, Cham. https://doi.org/10.1007/978-3-031-22356-3_6.
    [13] Hasimoto-Beltran, R., Canul-Ku, M., Méndez, G. M. D., Ocampo-Torres, F. J., & Esquivel-Trava, B. (2023). Ocean oil spill detection from SAR images based on multi-channel deep learning semantic segmentation. Marine Pollution Bulletin, 188, 114651.
    [14] Sun, Z., Yang, Q., Yan, N., Chen, S., Zhu, J., Zhao, J., & Sun, S. (2024). Utilizing deep learning algorithms for automated oil spill detection in medium resolution optical imagery. Marine Pollution Bulletin, 206, 116777.
    [15] Retrieved from Roboflow: https://universe.roboflow.com/yolo-m1lny/oil-detection-2
    [16] J. Redmon, S. Divvala, R. Girshick and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 779-788, doi: 10.1109/CVPR.2016.91.
    [17] Wang, CY., Yeh, IH., Mark Liao, HY. (2025). YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15089. Springer, Cham. https://doi.org/10.1007/978-3-031-72751-1_1.

Srinivas Talasila, Vijaya Kumar Gurrala, Varshini M, Siva Sai Balla, Madhuri M, and Chinthakindi Kiran Kumar(2025),Real-Time Oil Spill Detection with YOLO Framework for Marine Ecosystem Surveillance. IJEER 13(3), 463-470. DOI: 10.37391/IJEER.130310.