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Title: :  DEEPSPILLNET: A REAL-TIME AI FRAMEWORK FOR ENHANCED OIL SPILL DETECTION IN MARINE ENVIRONMENT USING YOLO-MOBILENET FUSION
PaperId: :  26103
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 3 2025
DUI:    16.0415/IJARIIE-26103
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
I. Z. YakubuDepartment of Computing Technology, SRM Institute of Science and Technology, India
Raymond DangdatFed Poly Kaltungo

Abstract

Science and Tech
Oil spill detection, Deep learning, YOLO-V3, MobileNet, Real-time monitoring and SAR imagery
This research presents DeepSpillNet, an advanced deep learning framework that combines YOLO-V3 and MobileNet architectures for real-time, high-accuracy oil spill detection in marine ecosystems. Addressing the limitations of conventional methods—such as slow processing, high false-alarm rates, and poor adaptability to dynamic ocean conditions—the proposed model leverages MobileNet’s lightweight feature extraction and YOLO-V3’s efficient object detection to achieve superior performance. Trained on Sentinel-1 SAR imagery, the system attains 94% precision and 91% recall in spill identification, with an inference time of just 45ms, enabling rapid response to environmental hazards. Comparative analysis demonstrates a 15% improvement in F1-score over existing CNN and SAR-based approaches, while maintaining computational efficiency for scalable deployment. The framework’s robustness across diverse spill sizes and environmental conditions makes it a practical tool for marine conservation agencies, aligning with global sustainability goals. Future work will explore multi-modal data integration and edge-computing optimization to further enhance real-world applicability.

Citations

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IJARIIE I. Z. Yakubu, and Raymond Dangdat. "DEEPSPILLNET: A REAL-TIME AI FRAMEWORK FOR ENHANCED OIL SPILL DETECTION IN MARINE ENVIRONMENT USING YOLO-MOBILENET FUSION" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 3 2025 Page 365-374
MLA I. Z. Yakubu, and Raymond Dangdat. "DEEPSPILLNET: A REAL-TIME AI FRAMEWORK FOR ENHANCED OIL SPILL DETECTION IN MARINE ENVIRONMENT USING YOLO-MOBILENET FUSION." International Journal Of Advance Research And Innovative Ideas In Education 11.3(2025) : 365-374.
APA I. Z. Yakubu, & Raymond Dangdat. (2025). DEEPSPILLNET: A REAL-TIME AI FRAMEWORK FOR ENHANCED OIL SPILL DETECTION IN MARINE ENVIRONMENT USING YOLO-MOBILENET FUSION. International Journal Of Advance Research And Innovative Ideas In Education, 11(3), 365-374.
Chicago I. Z. Yakubu, and Raymond Dangdat. "DEEPSPILLNET: A REAL-TIME AI FRAMEWORK FOR ENHANCED OIL SPILL DETECTION IN MARINE ENVIRONMENT USING YOLO-MOBILENET FUSION." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 3 (2025) : 365-374.
Oxford I. Z. Yakubu, and Raymond Dangdat. 'DEEPSPILLNET: A REAL-TIME AI FRAMEWORK FOR ENHANCED OIL SPILL DETECTION IN MARINE ENVIRONMENT USING YOLO-MOBILENET FUSION', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 3, 2025, p. 365-374. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/DEEPSPILLNET__A_REAL_TIME_AI_FRAMEWORK_FOR_ENHANCED_OIL_SPILL_DETECTION_IN_MARINE_ENVIRONMENT_USING_YOLO_MOBILENET_FUSION_ijariie26103.pdf (Accessed : ).
Harvard I. Z. Yakubu, and Raymond Dangdat. (2025) 'DEEPSPILLNET: A REAL-TIME AI FRAMEWORK FOR ENHANCED OIL SPILL DETECTION IN MARINE ENVIRONMENT USING YOLO-MOBILENET FUSION', International Journal Of Advance Research And Innovative Ideas In Education, 11(3), pp. 365-374IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/DEEPSPILLNET__A_REAL_TIME_AI_FRAMEWORK_FOR_ENHANCED_OIL_SPILL_DETECTION_IN_MARINE_ENVIRONMENT_USING_YOLO_MOBILENET_FUSION_ijariie26103.pdf (Accessed : )
IEEE I. Z. Yakubu, and Raymond Dangdat, "DEEPSPILLNET: A REAL-TIME AI FRAMEWORK FOR ENHANCED OIL SPILL DETECTION IN MARINE ENVIRONMENT USING YOLO-MOBILENET FUSION," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 3, pp. 365-374, May-Jun 2025. [Online]. Available: https://ijariie.com/AdminUploadPdf/DEEPSPILLNET__A_REAL_TIME_AI_FRAMEWORK_FOR_ENHANCED_OIL_SPILL_DETECTION_IN_MARINE_ENVIRONMENT_USING_YOLO_MOBILENET_FUSION_ijariie26103.pdf [Accessed : ].
Turabian I. Z. Yakubu, and Raymond Dangdat. "DEEPSPILLNET: A REAL-TIME AI FRAMEWORK FOR ENHANCED OIL SPILL DETECTION IN MARINE ENVIRONMENT USING YOLO-MOBILENET FUSION." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 3 ().
Vancouver I. Z. Yakubu, and Raymond Dangdat. DEEPSPILLNET: A REAL-TIME AI FRAMEWORK FOR ENHANCED OIL SPILL DETECTION IN MARINE ENVIRONMENT USING YOLO-MOBILENET FUSION. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : ]; 11(3) : 365-374. Available from: https://ijariie.com/AdminUploadPdf/DEEPSPILLNET__A_REAL_TIME_AI_FRAMEWORK_FOR_ENHANCED_OIL_SPILL_DETECTION_IN_MARINE_ENVIRONMENT_USING_YOLO_MOBILENET_FUSION_ijariie26103.pdf
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