Rethinking Lightweight RGB–Thermal Salient Object Detection With Local and Global Perception Network

RGB–thermal salient object detection (RGB-T SOD) aims to segment the most intriguing parts through RGB and thermal images. However, the high computational costs and large model sizes of existing methods prevent its deployment in edge computing platforms. To cope with it, a new lightweight RGB-T SOD method is proposed, namely, local and global perception network (LGPNet), which relieves the pressure of data transmission and processing and improves robustness in complicated surroundings. Specifically, high-level lightweight fusion (HLF) blocks and low-level lightweight fusion (LLF) blocks are designed to reduce the difference between two modalities and perform multimodal feature fusion. Unlike existing convolutional neural network-based lightweight SOD methods, HLF and LLF blocks combine the spatial inductive bias of convolutional neural networks with the global perception of Transformer, which is able to perform feature extraction and fusion with fewer parameters and larger receptive field. Experimental results show that our method is able to compete with the state-of-the-art RGB-T SOD methods while having only 7.35 learnable parameters[M], 6.40G floating-point operations and real-time speed (33 frames per second (FPS) on PyTorch framework and 224 FPS on TensorRT framework).