De Kerf Thomas, Sels Seppe, Samsonova Svetlana, Vanlanduit Steve
InViLab Research Group, Faculty of Applied Engineering, University of Antwerp, Antwerpen, Belgium.
Port of Antwerp-Bruges, Antwerp, Belgium.
Sci Data. 2024 Oct 30;11(1):1180. doi: 10.1038/s41597-024-03993-8.
The high incidence of oil spills in port areas poses a serious threat to the environment, prompting the need for efficient detection mechanisms. Utilizing automated drones for this purpose can significantly improve the speed and accuracy of oil spill detection. Such advancements not only expedite cleanup operations, reducing environmental harm but also enhance polluter accountability, potentially deterring future incidents. Currently, there's a scarcity of datasets employing RGB images for oil spill detection in maritime settings. This paper presents a unique, annotated dataset aimed at addressing this gap, leveraging a neural network for analysis on both desktop and edge computing platforms. The dataset, captured via drone, comprises 1268 images categorized into oil, water, and other, with a convolutional neural network trained using an Unet model architecture achieving an F1 score of 0.71 for oil detection. This underscores the dataset's practicality for real-world applications, offering crucial resources for environmental conservation in port environments.
港口区域石油泄漏的高发生率对环境构成了严重威胁，这促使需要高效的检测机制。为此利用自动无人机可以显著提高石油泄漏检测的速度和准确性。这些进步不仅加快了清理行动，减少了环境危害，还增强了污染者的责任追究，有可能威慑未来的事故。目前，在海洋环境中用于石油泄漏检测的采用RGB图像的数据集匮乏。本文提出了一个独特的、带注释的数据集，旨在填补这一空白，利用神经网络在桌面和边缘计算平台上进行分析。该数据集通过无人机采集，包含1268张分为油、水和其他类别的图像，使用Unet模型架构训练的卷积神经网络在石油检测方面的F1分数达到0.71。这突出了该数据集在实际应用中的实用性，为港口环境中的环境保护提供了关键资源。
