LSNet: Lightweight Spatial Boosting Network for Detecting Salient Objects in RGB-Thermal Images
Zhejiang University of Science and Technology · Nanyang Technological University · +3 more institutions
Abstract
Most recent methods for RGB (red-green-blue)-thermal salient object detection (SOD) involve several floating-point operations and have numerous parameters, resulting in slow inference, especially on common processors, and impeding their deployment on mobile devices for practical applications. To address these problems, we propose a lightweight spatial boosting network (LSNet) for efficient RGB-thermal SOD with a lightweight MobileNetV2 backbone to replace a conventional backbone (e.g., VGG, ResNet). To improve feature extraction using a lightweight backbone, we propose a boundary boosting algorithm that optimizes the predicted saliency maps and reduces information collapse in low-dimensional features. The…
Citation impact
- FWCI
- 27.73
- Percentile
- 100%
- References
- 82
Authors
5- WZWujie ZhouCorresponding
Zhejiang University of Science and Technology, Nanyang Technological University
- YZYun Zhu
Zhejiang University of Science and Technology, Nanjing University of Science and Technology
- JLJingsheng Lei
Zhejiang University of Science and Technology
- RYRongwang Yang
Children's Hospital of Zhejiang University
- LYLu Yu
Zhejiang University
Topics & keywords
- Computer science
- Boosting (machine learning)
- RGB color model
- Artificial intelligence
- Feature extraction
- Frame rate
- Backbone network
- Deep learning