LightSOD: Towards lightweight and efficient network for salient object detection

Abstract

The recent emphasis has been on achieving rapid and precise detection of salient objects, which presents a challenge for resource-constrained edge devices because the current models are too computationally demanding for deployment. Some recent research has prioritized inference speed over accuracy to address this issue. In response to the inherent trade-off between accuracy and efficiency, we introduce an innovative framework called LightSOD, with the primary objective of achieving a balance between precision and computational efficiency. LightSOD comprises several vital components, including the spatial-frequency boundary refinement module (SFBR), which utilizes wavelet transform to restore spatial loss information and capture edge features from the spatial-frequency domain. Additionally, we introduce a cross-pyramid enhancement module (CPE), which utilizes adaptive kernels to capture multi-scale group-wise features in deep layers. Besides, we introduce a group-wise semantic enhancement module (GSRM) to boost global semantic features in the topmost layer. Finally, we introduce a cross-aggregation module (CAM) to incorporate channel-wise features across layers, followed by a triple features fusion (TFF) that aggregates features from coarse to fine levels. By conducting experiments on five datasets and utilizing various backbones, we have demonstrated that LSOD achieves competitive performance compared with heavyweight cutting-edge models while significantly reducing computational complexity.