  Recently, relying on convolutional neural networks (CNNs), many methods for
salient object detection in optical remote sensing images (ORSI-SOD) are
proposed. However, most methods ignore the huge parameters and computational
cost brought by CNNs, and only a few pay attention to the portability and
mobility. To facilitate practical applications, in this paper, we propose a
novel lightweight network for ORSI-SOD based on semantic matching and edge
alignment, termed SeaNet. Specifically, SeaNet includes a lightweight
MobileNet-V2 for feature extraction, a dynamic semantic matching module (DSMM)
for high-level features, an edge self-alignment module (ESAM) for low-level
features, and a portable decoder for inference. First, the high-level features
are compressed into semantic kernels. Then, semantic kernels are used to
activate salient object locations in two groups of high-level features through
dynamic convolution operations in DSMM. Meanwhile, in ESAM, cross-scale edge
information extracted from two groups of low-level features is self-aligned
through L2 loss and used for detail enhancement. Finally, starting from the
highest-level features, the decoder infers salient objects based on the
accurate locations and fine details contained in the outputs of the two
modules. Extensive experiments on two public datasets demonstrate that our
lightweight SeaNet not only outperforms most state-of-the-art lightweight
methods but also yields comparable accuracy with state-of-the-art conventional
methods, while having only 2.76M parameters and running with 1.7G FLOPs for
288x288 inputs. Our code and results are available at
https://github.com/MathLee/SeaNet.
