Abstract
In this paper, AM-PSPNet is proposed for image semantic segmentation. AM-PSPNet embeds the efficient channel attention (ECA) module in the feature extraction stage of the convolutional network and makes the network pay more attention to the channels with obvious classification characteristics through end-to-end learning. To recognize the edges of objects and small objects more effectively, AM-PSPNet proposes a deep guidance fusion (DGF) module to generate global contextual attention maps to guide the expression of shallow information. The average crossover ratio of the proposed algorithm on the Pascal VOC 2012 dataset and Cityscapes dataset reaches 78.8% and 69.1%, respectively. Compared with the other four network models, the accuracy and average crossover ratio of AM-PSPNet are improved.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, J., Liu, B., Xu, K.: Semantic segmentation of high-resolution images. Sci. China Inf. Sci. 60(12), 1–6 (2017). https://doi.org/10.1007/s11432-017-9252-5
Yan, B., Niu, X., Bare, B., Tan, W.: Semantic segmentation guided pixel fusion for image retargeting. IEEE Trans. Multimedia 22, 676–687 (2020)
Zhao, Y., Qi, M., Li, X., Meng, Y., Yu, Y., Dong, Y.: P-LPN: toward real time pedestrian location perception in complex driving scenes. IEEE Access 8, 54730–54740 (2020)
Cheng, Z., Qu, A., He, X.: Contour-aware semantic segmentation network with spatial attention mechanism for medical image. Vis. Comput. 38(3), 749–762 (2021). https://doi.org/10.1007/s00371-021-02075-9
Zhang, R., Chen, J., Feng, L., Li, S., Yang, W., Guo, D.: A refined pyramid scene parsing network for polarimetric SAR image semantic segmentation in agricultural areas. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022)
Bai, S., Wang, C.: Information aggregation and fusion in deep neural networks for object interaction exploration for semantic segmentation. Knowl. Based Syst. 218, 106843 (2021)
Hao, S., Zhou, Y., Zhang, Y., Guo, Y.: Contextual attention refinement network for real-time semantic segmentation. IEEE Access 8, 55230–55240 (2020)
Ji, J., Lu, X., Luo, M., Yin, M., Miao, Q., Liu, X.: Parallel fully convolutional network for semantic segmentation. IEEE Access 9, 673–682 (2021)
Shelhamer, E., Long, J., Darrell, T.: Fully Convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 640–651 (2015)
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 2481–2495 (2017)
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
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
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: IEEE Computer Society (2016)
Lin, Z.K., Sun, W., Tang, B., Li, J.D., Yao, X.Y., Li, Y.: Semantic segmentation network with multipath structure, attention reweighting and multiscale encoding. Vis. Comput. 1−12 (2022). https://doi.org/10.1007/s00371-021-02360-7
Li, H., Qiu, K., Chen, L., et al.: SCAttNet: semantic segmentation network with spatial and channel attention mechanism for high-resolution remote sensing images. IEEE Geosci. Remote. Sens. Lett 18(5), 905–909 (2021)
Xia, Z., Kim, J.: Mixed spatial pyramid pooling for semantic segmentation. Appl. Soft Comput. 91, 106209 (2020)
Wang, Z., Wang, J., Yang, K., Wang, L., Su, F., Chen, X.: Semantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with Deeplabv3+. Comput. Geosci. 158, 104969 (2022)
Yin, J., Xia, P., He, J.: Online hard region mining for semantic segmentation. Neural Process. Lett. 50(3), 2665–2679 (2019). https://doi.org/10.1007/s11063-019-10047-3
Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11531–11539 (2020)
Jie, H., Li, S., Gang, S., Albanie, S.: Squeeze-and-excitation networks. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)
Wang, Y.-N., Tian, X., Zhong, G.: FFNet: feature fusion network for few-shot semantic segmentation. Cogn. Comput. 14(2), 1–12 (2022). https://doi.org/10.1007/s12559-021-09990-y
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wu, D., Zhao, J., Wang, Z. (2022). AM-PSPNet: Pyramid Scene Parsing Network Based on Attentional Mechanism for Image Semantic Segmentation. In: Wang, Y., Zhu, G., Han, Q., Wang, H., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2022. Communications in Computer and Information Science, vol 1628. Springer, Singapore. https://doi.org/10.1007/978-981-19-5194-7_32
Download citation
DOI: https://doi.org/10.1007/978-981-19-5194-7_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-5193-0
Online ISBN: 978-981-19-5194-7
eBook Packages: Computer ScienceComputer Science (R0)