A Fusion Underwater Salient Object Detection Based on Multi-Scale Saliency and Spatial Optimization

Abstract

Underwater images contain abundant information, but many challenges remain for underwater object detection tasks. Various salient object detection methods may encounter low detection precision, and the segmented map has an incomplete region of the target object. To deal with blurry underwater scenes and vague detection problems, a novel fusion underwater salient object detection algorithm (FUSOD) is proposed based on multi-scale saliency and spatial optimization. Firstly, an improved underwater color restoration was utilized to restore the color information for afterward color contrast saliency calculation. Secondly, a more accurate multi-scale fusion saliency map was obtained by fully considering both the global and local feature contrast information. Finally, the fusion saliency was optimized by the proposed spatial optimization method to enhance the spatial coherence. The proposed FUSOD algorithm may process turbid and complex underwater scenes and preserve a complete structure of the target object. Experimental results on the USOD dataset show that the proposed FUSOD algorithm can segment the salient object with a comparatively higher detection precision than the other traditional state-of-the-art algorithms. An ablation experiment showed that the proposed spatial optimization method increases the detection precision by 0.0325 scores in the F-Measure.