Year: 2025
Type: article
Abstract: To address the urgent need for rapid detection and precise segmentation of oil spill incidents, a cascaded processing framework integrating the YOLOv11 model with an enhanced Northern Goshawk Optimization (NGO) algorithm is proposed. This method effectively utilizes the advantages of deep learning and metaheuristic algorithms. Firstly, the YOLOv11 model was used for preliminary localization and segmentation of oil spill target regions in marine radar images. Subsequently, an improved NGO algorithm based on adaptive weighting factors, Levy flight perturbation, and pinhole imaging perturbation was used to finely segment the region, balancing processing efficiency and accuracy requirements. The experimental results showed that the cascade architecture proposed effectively balances the problems of false detection and missed detection. Compared with other methods, the marine radar oil film detection method based on YOLOv11 combined with improved NGO exhibited strong adaptability in complex scenes. Multiple indicators, such as accuracy, precision, recall, specificity, and Dice similarity coefficient, indicate that this method has good performance in marine radar oil spill detection tasks.
Source: Remote Sensing
DOI: 10.3390/rs17233922
Authors: Jin Xu, Yuanyuan Huang, Jin Yan, Zekun Guo, Bo Li, Haihui Dong, Peng Liu
Institutions: Guangdong Ocean University, Dalian Maritime University
Language: English
Subfield: Pollution
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W4416956462