MPSA: A multi-level pixel spatial attention network for thermal image segmentation based on Deeplabv3+ architecture

The thermal image segmentation has been widely concerned to alleviate the limitations of image segmentation caused by imaging of visible spectrum under challenge environmental conditions. The existing thermal image segmentation methods are not easy to obtain high quality segmented results, because thermal images are lack of color information, edges are unclear, details are not obvious. To this end, we proposed a multi-level pixel spatial attention network for thermal image segmentation. Specifically, we designed a pixel space attention module on each layer of the backbone network(MPAM), which recovers the more spatial detailed and maintains more semantic information. Then, we designed an edge extraction module (EEM) and a small target extraction module (STEM), which enhances the edge and small target features of the network by explicitly modeling the edge and small target features. Finally, the edge and small target features were fused with the output features of backbone, and the specialized loss functions was used to supervise them. Experimental results on SCUT-SEG, SODA and STI-Cityscpaes dataset demonstrate that our approach is slightly improved by 2.2% compared with other the state-of-art algorithms in the same scene.