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August 16, 2025

Using Convolutional Neural Network for the Detection of Offshore and Onshore Oil Spills

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Authors

ABA. O. BakareKSK. T. SerikiFEFagbemi Emmanuel

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Overview

Observational analysis demonstrates real-time oil spill detection using mobile phone apps, suggesting a cost-effective monitoring solution.

Key Points

  • The EfficientNet-Lite1 model achieved an impressive accuracy of 99.9% for oil spill detection.
  • Post-training quantization techniques optimized the model's size and inference speed effectively.
  • The Android application, developed with Flutter, utilizes the ML-Kit library for real-time predictions.
  • This approach may revolutionize environmental monitoring, enabling scalable and user-friendly solutions for oil spill response.

Cite This Study

Bakare et al. (2025) studied this question.

synapsesocial.com/papers/68a368710a429f797332d2a2https://doi.org/10.2118/228732-ms
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