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Local and Global Feature Aggregation-Aware Network for Salient Object Detection
| Content Provider | MDPI |
|---|---|
| Author | Da, Zikai Gao, Yu Xue, Zihan Cao, Jing Wang, Peizhen |
| Copyright Year | 2022 |
| Description | With the rise of deep learning technology, salient object detection algorithms based on convolutional neural networks (CNNs) are gradually replacing traditional methods. The majority of existing studies, however, focused on the integration of multi-scale features, thereby ignoring the characteristics of other significant features. To address this problem, we fully utilized the features to alleviate redundancy. In this paper, a novel CNN named local and global feature aggregation-aware network (LGFAN) has been proposed. It is a combination of the visual geometry group backbone for feature extraction, an attention module for high-quality feature filtering, and an aggregation module with a mechanism for rich salient features to ease the dilution process on the top-down pathway. Experimental results on five public datasets demonstrated that the proposed method improves computational efficiency while maintaining favorable performance. |
| Starting Page | 231 |
| e-ISSN | 20799292 |
| DOI | 10.3390/electronics11020231 |
| Journal | Electronics |
| Issue Number | 2 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-01-12 |
| Access Restriction | Open |
| Subject Keyword | Electronics Artificial Intelligence Salient Object Detection Cnns Local and Global Attention Aggregation |
| Content Type | Text |
| Resource Type | Article |
