Abstract
In this paper, we propose an efficient semantic segmentation network named Tiny Feature Map Network (TFMNet). This network significantly improves the running speed while achieves good accuracy. Our scheme uses a lightweight backbone network to extract primary features from input images of particular sizes. The hybrid dilated convolution framework and the DenseASPP module are used to alleviate the gridding problem. We evaluate the proposed network on the Cityscapes and CamVid datasets, and obtain performance comparable with the existing state-of-the-art real-time semantic segmentation methods. Specifically, it achieves \(72.9\%\) mIoU on the Cityscapes test dataset with only 2.4M parameters and a speed of 113 FPS on NVIDIA GTX 1080 Ti without pre-training on the ImageNet dataset.
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Huang, H. et al. (2020). An Efficient Tiny Feature Map Network for Real-Time Semantic Segmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_26
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DOI: https://doi.org/10.1007/978-3-030-64559-5_26
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