Abstract
Streetscape scene understanding is an important task for semantic segmentation. In this paper, we propose a PFANet improved based on PSPNet for Street Semantic Segmentation. For further improving the feature fusion capability of our model, we added a feature fusion module to PSPNet to incorporate features of different dimensions. Meanwhile, we introduce an attention mechanism, using a combination of spatial attention mechanism and channel attention mechanism attention module to enhance the contextual dependencies of local features and the spatial interdependencies of features. We experimented on the Cityscapes dataset, and achieved a mIoU score of 80.38% on Cityscapes validation dataset which improved 0.68% than PSPNet.
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References
Davis, L.S., Rosenfeld, A., Weszka, J.S.: Region extraction by averaging and thresholding. IEEE Trans. Syst. Man Cybern. SMC-5(3), 383–388 (1975). https://doi.org/10.1109/TSMC.1975.5408419
Senthilkumaran, N., Rajesh, R.: Edge detection techniques for image segmentation– a survey of soft computing approaches. Int. J. Recent Trends Eng. 1(2) (2009)
Ng, H.P., Ong, S.H., Foong, K.W.C., Goh, P.S., Nowinski, W.L.: Medical image segmentation using k-means clustering and improved watershed algorithm. In: 2006 IEEE Southwest Symposium on Image Analysis and Interpretation, Denver, CO, pp. 61–65 (2006)
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2881–2890 (2017)
Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. CoRR, abs/1606.00915 (2016)
Chen, L.-C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. CoRR, abs/1706.05587 (2017)
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: efficient channel attention for deep convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
Wang, X., Ross, G., Abhinav, G., He, K.: Non-local neural networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
Cao, Y., Xu, J., Lin, S., Wei, F., Hu, H.: Gcnet: non-local networks meet squeeze-excitation networks and beyond. In: ICCV Workshops (2019)
Park, J., Woo, S., Lee, J.-Y., Kweon, I.S.: Bam: bottleneck attention module. In: British Machine Vision Conference (BMVC) (2018)
Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding (2016)
MMSegmentation Contributors. MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark (2020). https://github.com/open-mmlab/mmsegmentation
He, T., et al.: Bag of tricks for image classification with convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019)
Zhou, Z., RahmanSiddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: a nested u-net architecture for medical image segmentation. In: Stoyanov, Danail, et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_1
Sun, K., et al.: High-resolution representations for labeling pixels and regions arXiv:1904.04514 (2019)
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Ge, J., Li, Y., Jiu, M., Cheng, Z., Zhang, J. (2023). PFANet: A Network Improved by PSPNet for Semantic Segmentation of Street Scenes. In: Liang, Q., Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2022. Lecture Notes in Electrical Engineering, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-99-1260-5_16
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