Marine Radar Oil Spill Detection Method Based on RBM and Improved Quantum Golden Jackal Optimization Algorithm

Marine ecological systems are severely compromised by oil spill incidents, while oceanic economic growth and public well-being are also put at considerable risk. Therefore, the development of high-efficiency and precise detection techniques for marine pollution prevention and control holds substantial practical importance. A marine radar oil spill detection approach is presented herein, integrating Restricted Boltzmann Machine (RBM) feature learning with the Improved Quantum Golden Jackal Optimization (IQGJO) algorithm. Initially, raw radar imagery is preprocessed. Subsequently, unsupervised deep feature extraction is executed via RBM, and oil spill regions of interest are identified using K-means clustering. Ultimately, the IQGJO algorithm is employed to perform adaptive threshold optimization, along with fine segmentation of oil film targets. When benchmarked against conventional Golden Jackal Optimization (GJO) and gravity search algorithm (GSA), quantum mechanisms, along with Lévy flight perturbation, are incorporated within IQGJO. Consequently, the tendency of traditional approaches to converge toward high-gray local optima is effectively mitigated. Moreover, recognition capability for both weak-reflection and fragmented oil films is substantially enhanced. Experimental outcomes reveal that an accuracy of 99.7% is achieved by the IQGJO algorithm, representing a marked improvement over both GJO and GSA counterparts. High-precision automated segmentation of surface oil spills under intricate maritime conditions is thus enabled, while effective technical guidance is offered for radar-based oil spill surveillance and emergency response.