Abstract
The performance of salient object detection has been significantly advanced by using fully convolutional networks (FCN). However, it still remains nontrivial to take full advantage of the multi-level convolutional features for salient object detection. In this paper, a dense grid network framework (denoted \(\mathbf {DGrid}\)) is proposed to solve the above problem, which mainly consists of the backbone module, extended module and fusion module. Specifically, \(\mathbf {DGrid}\) utilizes a multi-branch refinement mechanism for saliency detection. First, the backbone module is used to generate a coarse prediction map. Then, the extended module, which contains four branches, is used to improve the resolution and precision of the prediction map gradually from coarse to fine. Moreover, we proposed the densely connected strategy to fully fuse features at different levels. Finally, the fusion module densely fuses the highest level features of all branches to achieve the final saliency map. Experimental results on five widely used benchmark datasets demonstrate that \(\mathbf {DGrid}\) can improve the accuracy of detection by maintaining a high-resolution feature branch, and it outperforms state-of-the-art approaches without any post-processing.
This work supported by Science and Technology Project of State Grid Fujian Electric Power Co., Ltd. under grant 52130M19000X and National Natural Science Foundation of China (NSFC) under grant 61873067.
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Cai, Y. et al. (2022). DGrid: Dense Grid Network for Salient Object Detection. In: Sun, F., Hu, D., Wermter, S., Yang, L., Liu, H., Fang, B. (eds) Cognitive Systems and Information Processing. ICCSIP 2021. Communications in Computer and Information Science, vol 1515. Springer, Singapore. https://doi.org/10.1007/978-981-16-9247-5_18
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