ABSTRACT:

Marine oil spill detection using Synthetic Aperture Radar (SAR) is crucial but challenged by dynamic marine conditions, diverse spill scales, and limitations in existing algorithms regarding model size and real-time performance. We introduce LSFE-YOLO, a lightweight marine oil spill detection model built upon an optimized YOLOv8s architecture. The SAR images we used were obtained from the Alaska Satellite Facility (ASF) platform. Publicly available oil spill data were obtained from the National Oceanic and Atmospheric Administration (NOAA), including information such as the geographic coordinates, date of occurrence, and image source of the oil spill. NOAA (www.ospo.noaa.gov/Products/ocean/) has published verified oil spill data from the Gulf of Mexico, the Pacific Ocean, the Atlantic Ocean, the Great Lakes, and international waters, utilizing Sentinel-1 satellites to gather information on various types of oil spills. The corresponding Sentinel-1 SAR images can be obtained from the ASF (https://search.asf.alaska.edu/) platform using information such as the geographical coordinates and occurrence dates of oil spill incidents. Sentinel-1 monitors the surface in all weather conditions and provides high-quality SAR images, making it an ideal data source for our research. Our approach integrates FasterNet into the backbone and adjusts the neck network width to reduce memory overhead and parameters, significantly boosting detection speed. We also propose a novel GroupNorm and Lightweight Shared Convolution (GN-LSC) Head detection module that features a shared convolutional structure. This structure not only decreases the number of parameters in the detection head but also minimizes redundant computations.