Abstract
Extensive growth in the field of robotics and autonomous industries, the demand for efficient image segmentation is increasing rapidly. Whilst existing methods have been shown to achieve outstanding results on challenging data sets, they cannot scale the model properly for real-world computational constraints applications due to a fixed large backbone network. We propose a novel architecture for semantic scene segmentation suitable for resource-constrained applications. Specifically, we make use of the global contextual prior by using a pyramid pooling technique on top of the backbone network. We also employ the recently proposed EfficientNet network to make our model efficiently scalable for computational constraints. We show that our newly proposed model - Efficient Segmentation Pyramid Network (ESPNet) - outperforms many existing scene segmentation models and produces 88.5% pixel accuracy on validation and 80.9% on training set of the Cityscapes benchmark.
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References
Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. TPAMI 39(12), 2481–2495 (2017)
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. TPAMI 40(4), 834–848 (2017)
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of ICCV, September 2018
Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of CVPR, June 2016
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. TPAMI 37(9), 1904–1916 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of CVPR, June 2016
Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of CVPR, June 2018
Kendall, A., Badrinarayanan, V., Cipolla, R.: Bayesian segnet: model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. arXiv preprint arXiv:1511.02680 (2015)
Paszke, A., Chaurasia, A., Kim, S., Culurciello, E.: Enet: a deep neural network architecture for real-time semantic segmentation. arXiv preprint arXiv:1606.02147 (2016)
Poudel, R.P., Bonde, U., Liwicki, S., Zach, C.: Contextnet: exploring context and detail for semantic segmentation in real-time. arXiv preprint arXiv:1805.04554 (2018)
Poudel, R.P., Liwicki, S., Cipolla, R.: Fast-scnn: fast semantic segmentation network. arXiv preprint arXiv:1902.04502 (2019)
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
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv 2: inverted residuals and linear bottlenecks. In: Proceedings of CVPR (June 2018)
Shang, Y., Zhong, S., Gong, S., Zhou, L., Ying, W.: DXNet: an encoder-decoder architecture with XSPP for semantic image segmentation in street scenes. In: Gedeon, T., Wong, K.W., Lee, M. (eds.) ICONIP 2019. CCIS, vol. 1143, pp. 550–557. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36802-9_59
Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. TPAMI 39(4), 640–651 (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946 (2019)
Targ, S., Almeida, D., Lyman, K.: Resnet in resnet: generalizing residual architectures. arXiv preprint arXiv:1603.08029 (2016)
Yang, M., Shi, Y.: DSMRSeg: dual-stage feature pyramid and multi-range context aggregation for real-time semantic Segmentation. In: Gedeon, T., Wong, K.W., Lee, M. (eds.) ICONIP 2019. CCIS, vol. 1142, pp. 265–273. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36808-1_29
Zhao, H., Qi, X., Shen, X., Shi, J., Jia, J.: ICNet for real-time semantic segmentation on high-resolution images. In: Proceedings of ECCV, September 2018
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of CVPR, July 2017
Acknowledgement
The authors would like to acknowledge Pawsey supercomputing centre for providing J. Dunstan the internship during which part of the work was done.
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Singha, T., Pham, DS., Krishna, A., Dunstan, J. (2020). Efficient Segmentation Pyramid Network. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_44
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