A new approach to oil spill detection that combines deep learning with unmanned aerial vehicles

Abstract

This study presents a novel approach to automatic oil spill detection, using unmanned aerial vehicle (UAV) images to realize intelligent control in oil production. Despite considerable effort, oil spills still cannot be detected automatically and effectively due to the complexity of the real production environment, which forces oil enterprises to manually inspect facilities and detect oil spills. To solve the problem, we propose an approach consisting of UAVs, deep learning and traditional algorithms—an approach which divides the oil spill detection task into three independent sub-tasks. First, we constructed a model based on the deep convolutional neural network, which can quickly detect the suspected oil spill area in images to ensure there are no omissions. Second, to remove other obstacles in the images, we adjusted the Otsu algorithm to filter the detection results, which improves precision while not affecting the recall rate. Third, the Maximally Stable Extremal Regions algorithm was used to obtain the detail polygon region from the detection box, thus automatically evaluating the severity of the oil spill. Experiments showed that our method could solve problems effectively, reducing the cost of oil spill detection by 57.2% when compared with the traditional manual inspection process.