Abstract
Image semantic segmentation is the basis of performing various tasks in computer vision. It has been widely used in medical imaging, robotics and many other fields. However, the existing image semantic segmentation technology cannot improve the segmentation speed while ensuring the segmentation accuracy, and cannot meet the requirements of real-time applications. Therefore, this paper proposes a real-time image semantic segmentation method based on dual efficient attention mechanism (DEANet). Pyramid sampling is introduced into the channel dimension to extract multi-scale information, and higher resolution aggregation features are adopted as the input of the spatial dimension. It can achieve high efficiency and accuracy of image semantic segmentation. The proposed DEANet was tested on two classic datasets. On the Cityscapes dataset, when the input size is 512 × 1024, the segmentation accuracy reaches 74.90% mIoU, and the segmentation speed reaches 99.91FPS. On the CamVid dataset, when the input size is 360 × 480, the segmentation accuracy reaches 70.07% mIoU and the segmentation speed reaches 142.72 FPS.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (Grant No. U1908214), Special Project of Central Government Guiding Local Science and Technology Development (Grant No. 2021JH6/10500140), Program for the Liaoning Distinguished Professor, Program for Innovative Research Team in University of Liaoning Province (Grant No. LT2020015), the Support Plan for Key Field Innovation Team of Dalian (2021RT06), the Science and Technology Innovation Fund of Dalian (Grant No. 2020JJ25CY001), the Support Plan for Leading Innovation Team of Dalian University (XLJ202010), Dalian University Scientific Research Platform Project (No. 202101YB03).
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Liu, X., Liu, R., Dong, J., Yi, P., Zhou, D. (2022). DEANet: A Real-Time Image Semantic Segmentation Method Based on Dual Efficient Attention Mechanism. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13472. Springer, Cham. https://doi.org/10.1007/978-3-031-19214-2_16
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