AI-Enhanced Real-Time Monitoring of Marine Pollution: Part 2—A Spectral Analysis Approach
Oil spills and marine litter pose significant threats to marine ecosystems, necessitating innovative real-time monitoring solutions. This research presents a novel AI-driven multisensor system that integrates RGB, thermal infrared, and hyperspectral radiometers to detect and classify pollutants in d...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/13/4/636 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850143771786739712 |
|---|---|
| author | Navya Prakash Oliver Zielinski |
| author_facet | Navya Prakash Oliver Zielinski |
| author_sort | Navya Prakash |
| collection | DOAJ |
| description | Oil spills and marine litter pose significant threats to marine ecosystems, necessitating innovative real-time monitoring solutions. This research presents a novel AI-driven multisensor system that integrates RGB, thermal infrared, and hyperspectral radiometers to detect and classify pollutants in dynamic offshore environments. The system features a dual-unit design: an overview unit for wide-area detection and a directional unit equipped with an autonomous pan-tilt mechanism for focused high-resolution analysis. By leveraging multi-hyperspectral data fusion, this system overcomes challenges such as variable lighting, water surface reflections, and environmental interferences, significantly enhancing pollutant classification accuracy. The YOLOv5 deep learning model was validated using extensive synthetic and real-world marine datasets, achieving an F1-score of 0.89 and an mAP of 0.90. These results demonstrate the robustness and scalability of the proposed system, enabling real-time pollution monitoring, improving marine conservation strategies, and supporting regulatory enforcement for environmental sustainability. |
| format | Article |
| id | doaj-art-5f926e16490846ea873b299d46b39b00 |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-5f926e16490846ea873b299d46b39b002025-08-20T02:28:36ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-03-0113463610.3390/jmse13040636AI-Enhanced Real-Time Monitoring of Marine Pollution: Part 2—A Spectral Analysis ApproachNavya Prakash0Oliver Zielinski1Marine Sensor Systems, Institute for Chemistry and Biology of the Marine Environment (ICBM), Carl von Ossietzky University of Oldenburg, 26129 Oldenburg, GermanyMarine Sensor Systems, Institute for Chemistry and Biology of the Marine Environment (ICBM), Carl von Ossietzky University of Oldenburg, 26129 Oldenburg, GermanyOil spills and marine litter pose significant threats to marine ecosystems, necessitating innovative real-time monitoring solutions. This research presents a novel AI-driven multisensor system that integrates RGB, thermal infrared, and hyperspectral radiometers to detect and classify pollutants in dynamic offshore environments. The system features a dual-unit design: an overview unit for wide-area detection and a directional unit equipped with an autonomous pan-tilt mechanism for focused high-resolution analysis. By leveraging multi-hyperspectral data fusion, this system overcomes challenges such as variable lighting, water surface reflections, and environmental interferences, significantly enhancing pollutant classification accuracy. The YOLOv5 deep learning model was validated using extensive synthetic and real-world marine datasets, achieving an F1-score of 0.89 and an mAP of 0.90. These results demonstrate the robustness and scalability of the proposed system, enabling real-time pollution monitoring, improving marine conservation strategies, and supporting regulatory enforcement for environmental sustainability.https://www.mdpi.com/2077-1312/13/4/636marine pollutiondeep learningspectral analysissynthetic data |
| spellingShingle | Navya Prakash Oliver Zielinski AI-Enhanced Real-Time Monitoring of Marine Pollution: Part 2—A Spectral Analysis Approach Journal of Marine Science and Engineering marine pollution deep learning spectral analysis synthetic data |
| title | AI-Enhanced Real-Time Monitoring of Marine Pollution: Part 2—A Spectral Analysis Approach |
| title_full | AI-Enhanced Real-Time Monitoring of Marine Pollution: Part 2—A Spectral Analysis Approach |
| title_fullStr | AI-Enhanced Real-Time Monitoring of Marine Pollution: Part 2—A Spectral Analysis Approach |
| title_full_unstemmed | AI-Enhanced Real-Time Monitoring of Marine Pollution: Part 2—A Spectral Analysis Approach |
| title_short | AI-Enhanced Real-Time Monitoring of Marine Pollution: Part 2—A Spectral Analysis Approach |
| title_sort | ai enhanced real time monitoring of marine pollution part 2 a spectral analysis approach |
| topic | marine pollution deep learning spectral analysis synthetic data |
| url | https://www.mdpi.com/2077-1312/13/4/636 |
| work_keys_str_mv | AT navyaprakash aienhancedrealtimemonitoringofmarinepollutionpart2aspectralanalysisapproach AT oliverzielinski aienhancedrealtimemonitoringofmarinepollutionpart2aspectralanalysisapproach |