  Salient object detection in optical remote sensing images (ORSI-SOD) has been
widely explored for understanding ORSIs. However, previous methods focus mainly
on improving the detection accuracy while neglecting the cost in memory and
computation, which may hinder their real-world applications. In this paper, we
propose a novel lightweight ORSI-SOD solution, named CorrNet, to address these
issues. In CorrNet, we first lighten the backbone (VGG-16) and build a
lightweight subnet for feature extraction. Then, following the coarse-to-fine
strategy, we generate an initial coarse saliency map from high-level semantic
features in a Correlation Module (CorrM). The coarse saliency map serves as the
location guidance for low-level features. In CorrM, we mine the object location
information between high-level semantic features through the cross-layer
correlation operation. Finally, based on low-level detailed features, we refine
the coarse saliency map in the refinement subnet equipped with Dense
Lightweight Refinement Blocks, and produce the final fine saliency map. By
reducing the parameters and computations of each component, CorrNet ends up
having only 4.09M parameters and running with 21.09G FLOPs. Experimental
results on two public datasets demonstrate that our lightweight CorrNet
achieves competitive or even better performance compared with 26
state-of-the-art methods (including 16 large CNN-based methods and 2
lightweight methods), and meanwhile enjoys the clear memory and run time
efficiency. The code and results of our method are available at
https://github.com/MathLee/CorrNet.
