  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.
