Abstract
Although deep convolutional networks have achieved good results in the field of salient object detection, most of these methods can not work well near the boundary. This results in poor boundary quality of network predictions, accompanied by a large number of blurred contours and hollow objects. To solve this problem, this paper proposes a Boundary Enhance Network (BENet) for salient object detection, which makes the network pay more attention to salient edge features by fusing auxiliary boundary information of objects. We adopt the Progressive Feature Extraction Module (PFEM) to obtain multi-scale edge and object features of salient objects. In response to the semantic gap problem in feature fusion, we propose an Adaptive Edge Fusion Module (AEFM) to allow the network to adaptively and complementarily fuse edge features and salient object features. The Self Refinement (SR) module further repairs and enhances edge features. Moreover, in order to make the network pay more attention to the boundary, we design an edge enhance loss function, which uses the additional boundary maps to guide the network to learn rich boundary features at the pixel level. Experimental results show that our proposed method outperforms state-of-the-art methods on five benchmark datasets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhao, J.X., Liu, J.J., Fan, D.P., et al.: EGNet: edge guidance network for salient object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8779–8788 (2019)
Qin, X., Zhang, Z., Huang, C., et al.: Basnet: boundary-aware salient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7479–7489 (2019)
Li, X., Yang, F., Cheng, H., et al.: Contour knowledge transfer for salient object detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 355–370 (2018)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)
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
Zhao, T., Wu, X.: Pyramid feature attention network for saliency detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3085–3094 (2019)
Wang, W., Zhao, S., Shen, J., et al.: Salient object detection with pyramid attention and salient edges. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1448–1457 (2019)
Hou, Q., Cheng, M.M., Hu, X., et al.: Deeply supervised salient object detection with short connections. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3203–3212 (2017)
Zhao, R., Ouyang, W., Li, H., et al.: Saliency detection by multi-context deep learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1265–1274 (2015)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Lee, G., Tai, Y.W., Kim, J.: Deep saliency with encoded low level distance map and high level features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 660–668 (2016)
Chen, Z., Xu, Q., Cong, R., et al.: Global context-aware progressive aggregation network for salient object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 07, pp. 10599–10606 (2020)
Liu, J.J., Hou, Q., Cheng, M.M., et al.: A simple pooling-based design for real-time salient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3917–3926 (2019)
Zhou, L., Gu, X.: Embedding topological features into convolutional neural network salient object detection. Neural Netw. 121, 308–318 (2020)
Wu, Z., Su, L., Huang, Q.: Cascaded partial decoder for fast and accurate salient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3907–3916 (2019)
Wei, J., Wang, S., Huang, Q.: F\(^3\)Net: fusion, feedback and focus for salient object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 07, pp. 12321–12328 (2020)
Li, J., Su, J., Xia, C., et al.: Salient object detection with purificatory mechanism and structural similarity loss. IEEE Trans. Image Process. 30, 6855–6868 (2021)
Deng, Z., Hu, X., Zhu, L., et al.: R3net: recurrent residual refinement network for saliency detection. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, Menlo Park, CA, USA, AAAI Press, pp. 684–690 (2018)
Zhang, P., Wang, D., Lu, H., et al.: Amulet: aggregating multi-level convolutional features for salient object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 202–211 (2017)
Liu, N., Han, J., Yang, M.H.: Picanet: learning pixel-wise contextual attention for saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3089–3098 (2018)
Zhao, X., Pang, Y., Zhang, L., Lu, H., Zhang, L.: Suppress and balance: a simple gated network for salient object detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 35–51. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58536-5_3
Mohammadi, S., Noori, M., Bahri, A., et al.: CAGNet: content-aware guidance for salient object detection. Pattern Recogn. 103, 107303 (2020)
Liu, Y., Zhang, Q., Zhang, D., et al.: Employing deep part-object relationships for salient object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1232–1241 (2019)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant 62076183, Grant 61936014, and Grant 61976159; in part by the Natural Science Foundation of Shanghai under Grant 20ZR1473500 and Grant 19ZR1461200; in part by the Shanghai Innovation Action Project of Science and Technology under Grant 20511100700; in part by the National Key Research and Development Project under Grant 2019YFB2102300 and Grant 2019YFB2102301; in part by the Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX0100; and in part by the Fundamental Research Funds for the Central Universities. The authors would also like to thank the anonymous reviewers for their careful work and valuable suggestions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yan, Z., Liang, S. (2023). BENet: Boundary Enhance Network for Salient Object Detection. In: Dang-Nguyen, DT., et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13834. Springer, Cham. https://doi.org/10.1007/978-3-031-27818-1_19
Download citation
DOI: https://doi.org/10.1007/978-3-031-27818-1_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-27817-4
Online ISBN: 978-3-031-27818-1
eBook Packages: Computer ScienceComputer Science (R0)