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.

翻译：港口区域溢油事件频发，对环境构成严重威胁，亟需高效的检测机制。利用自动化无人机进行检测可显著提升溢油检测的速度和精度。这一进步不仅能加快清理作业、减少环境损害，还能加强污染者责任追究，从而可能遏制未来事故的发生。当前，利用RGB图像进行海洋环境溢油检测的数据集十分稀缺。本文提出一个独特且经过标注的数据集，旨在填补这一空白，并通过神经网络在桌面和边缘计算平台上进行分析。该数据集由无人机拍摄，包含1268张图像，分为油、水和其他三类，采用Unet模型架构训练的卷积神经网络在溢油检测上取得了0.71的F1分数。这突显了该数据集在实际应用中的实用性，为港口环境保护提供了关键资源。