Monitoring offshore oil pollution using multi-class convolutional neural networks

Abstract

Oil and gas production operations are a major source of environmental pollution that expose people and habitats in many coastal communities around the world to adverse health effects. Detecting oil spills in a timely and precise manner can help improve the oil spill response process and channel required resources more effectively to affected regions. In this research, convolutional neural networks, a branch of artificial intelligence (AI), are trained on a visual dataset of oil spills containing images from different altitudes and geographical locations. In particular, a VGG16 model is adopted through transfer learning for oil spill classification (i.e., detecting if there is oil spill in an image) with an accuracy of 92%. Next, Mask R–CNN and PSPNet models are used for oil spill segmentation (i.e., pixel-level detection of oil spill boundaries) with a mean intersection over union (IoU) of 49% and 68%, respectively. Lastly, to determine if there is an oil rig or vessel in the vicinity of a detected oil spill and provide a holistic view of the oil spill surroundings, a YOLOv3 model is trained and used, yielding a maximum mean average precision (mAP) of ~71%. Findings of this research can improve the current practices of oil pollution cleanup and predictive maintenance, ultimately leading to more resilient and healthy coastal communities.