What question did this study set out to answer?

The aim is to enhance the detection and segmentation of oil spills in marine radar images using an improved optimization algorithm.

February 8, 2026Open Access

Novel Hybrid Feature Engineering with Optimized BAS Algorithm for Shipborne Radar Marine Oil Spill Detection

JXJiayi XuZhejiang International Studies University BXBo XuGuangdong Ocean University HDHaihui DongGuangdong Ocean University

Key Points

The aim is to enhance the detection and segmentation of oil spills in marine radar images using an improved optimization algorithm.
Transformed raw radar images into Cartesian coordinates and applied filtering to reduce interference.
Improved gray distribution and contrast of images through denoising techniques.
Quantified complexity of grayscale distributions using Shannon entropy to select important features.
Refined regions of interest using Otsu thresholding for wave segmentation.
Implemented an improved BAS algorithm with enhanced accuracy for oil film segmentation.
Achieved an intersection over union (IOU) of 81.2% in oil spill detection.
Reached a Dice score of 89.6%, indicating high performance in segmentation.
Recorded a recall of 85.2% and F1-score of 90.1%, showcasing effectiveness.
Significantly improved detection accuracy compared to traditional methods.

Abstract

Offshore oil exploration and the volume of imported crude oil shipping have increased steadily, elevating the risk of oil spills. An advanced offshore oil film identification method is proposed to realize the accurate and robust recognition and segmentation of oil films from marine radar images in offshore oil spill detection. This method integrates feature engineering with an improved Beetle Antennae Search (BAS) optimization algorithm, aiming to address the key issues of low discrimination between oil films and complex marine backgrounds and insufficient spill boundary localization accuracy in radar image analysis. First, the raw radar image was transformed into the Cartesian coordinate system, and a filtering procedure was applied to attenuate interference. Subsequently, the gray distribution and local contrast of the denoised image was further improved. Afterwards, the complexity of the grayscale distribution within each feature map was quantified using Shannon entropy. The Top-K feature maps with the highest entropy values were subsequently used to construct an information-rich subset. The subset was then processed through a pixel-wise averaging strategy to generate a coupled feature image. Then, Otsu threshold was used to refine ocean wave regions. Finally, the oil films were segmented with an improved BAS optimization algorithm. The fitness function of the improved BAS algorithm was augmented through the integration of edge fitting accuracy, and a target-proximity penalization scheme. Through an adaptive step-length modulation paradigm and Perceptual Mechanism, it can achieve a marked improvement in search accuracy and achieving precise segmentation of oil slicks. The detection accuracy of the proposed method is significantly enhanced relative to the traditional BAS algorithm and existing marine radar oil spill detection methods. The IOU, Dice, recall and F1-score reached 81.2%, 89.6%, 85.2%, and 90.1% respectively. This method not only advances the methodological rigor of spill detection but also provides critical data support for the development of more effective control and remediation practices.

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