A dataset of drone-captured, segmented images for oil spill detection in port environments

Abstract

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.