Polarization spatial and semantic learning lightweight network for underwater salient object detection

The absorption by a water body and the scattering of suspended particles cause blurring of object features, which results in a reduced accuracy of underwater salient object detection (SOD). Thus, we propose a polarization spatial and semantic learning lightweight network for underwater SOD. The proposed method is based on a lightweight MobileNetV2 network. Because lightweight networks are not as capable as deep networks in capturing and learning features of complex objects, we build specific feature extraction and fusion modules at different depth stages of backbone network feature extraction to enhance the feature learning capability of the lightweight backbone network. Specifically, we embed a structural feature learning module in the low-level feature extraction stage and a semantic feature learning module in the high-level feature extraction stage to maintain the spatial consistency of low-level features and the semantic commonality of high-level features. We acquired polarized images of underwater objects in natural underwater scenes and constructed a polarized object detection dataset (PODD) for object detection in the underwater environment. Experimental results show that the detection effect of the proposed method on the PODD is better than other SOD methods. Also, we conduct comparative experiments on the RGB-thermal (RGB-T) and RGB-depth (RGB-D) datasets to verify the generalization of the proposed method.