基于渐进式嵌套特征的融合网络

doi:10.16451/j.cnki.issn1003-6059.202301006

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Abstract In salient object detection, the computer detects the most interesting areas or objects in the visual scene by means of introducing the human visual attention mechanism. Aiming at the problems of unclear edge, incomplete object and missing detection of small objects in salient object detection, a fusion network based on progressive nested feature is proposed. Progressive compression module is adopted to continuously transfer and merge deeper features downward and make full use of advanced semantic information while the number of model parameters is reduced. A weighted feature fusion module is designed to aggregate the multi-scale features of the encoder into a feature map that can access both high-level and low-level information. Then, the aggregated features are allocated to other layers to fully obtain image context information and focus on small objects in the image. The asymmetric convolution block is introduced to further improve the detection accuracy. Experiments on six open datasets show that the proposed network achieves good detection results.

Key words： Key Words Salient Object Detection Feature Pyramid Network Progressive Compression Weighted Feature Fusion

Received: 20 September 2022

ZTFLH:

TP391

Fund:General Program of National Natural Science Foundation of China(No.62276092), Key Science and Technology Program of Henan Province(No.212102310084), Key Scientific Research Projects of Colleges and Universities in Henan Province(No.22A520027)

Corresponding Authors: SUN Junding, Ph.D., professor. His research interests include image processing and pattern recognition.

About author:: WANG Jinkai, master student. His research interests include salient object detection.TANG Chaosheng, Ph.D., lecturer. His research interests include medical image processing.WU Xiaosheng, master, associate professor. Her research interests include image processing and pattern recognition.

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	SUN Junding
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Cite this article:

SUN Junding,WANG Jinkai,TANG Chaosheng等. Fusion Network Based on Progressive Nested Feature[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(1): 70-80.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202301006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2023/V36/I1/70

[1] JIANG H Z, WANG J D, YUAN Z J, et al. Salient Object Detection: A Discriminative Regional Feature Integration Approach // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2013: 2083-2090.
[2] QIN X B, ZHANG Z C, HUANG C Y, et al. BASNet: Boundary-Aware Salient Object Detection // Proc of the IEEE/CVF Confe-rence on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 7471-7481.
[3] LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature Pyramid Networks for Object Detection // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2017: 936-944.
[4] LIU N, HAN J W, ZHANG D W, et al. Predicting Eye Fixations Using Convolutional Neural Networks // Proc of the IEEE Confe-rence on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2015: 362-370.
[5] LI G B, YU Y Z.Visual Saliency Based on Multiscale Deep Features // Proc of the IEEE Conference on Computer Vision and Pa-ttern Recognition. Washington, USA: IEEE, 2015: 5455-5463.
[6] LI X, ZHAO L M, WEI L N, et al. DeepSaliency: Multi-task Deep Neural Network Model for Salient Object Detection. IEEE Transactions on Image Processing, 2016, 25(8): 3919-3930.
[7] HOU Q B, CHENG M M, HU X W, et al. Deeply Supervised Salient Object Detection with Short Connections. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(4): 815-828.
[8] XIE S N, TU Z W.Holistically-Nested Edge Detection // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2015: 1395-1403.
[9] ZHAO T, WU X Q.Pyramid Feature Attention Network for Saliency Detection // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 3080-3089.
[10] GAO S H, TAN Y Q, CHENG M M, et al. Highly Efficient Sa-lient Object Detection with 100k Parameters // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2020: 702-721.
[11] WU Z, SU L, HUANG Q M.Cascaded Partial Decoder for Fast and Accurate Salient Object Detection // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 3902-3911.
[12] WU Z, SU L, HUANG Q M.Stacked Cross Refinement Network for Edge-Aware Salient Object Detection // Proc of the IEEE/CVF International Conference on Computer Vision. Washington, USA: IEEE, 2019: 7263-7272.
[13] LIU J J, HOU Q B, CHENG M M, et al. A Simple Pooling-Based Design for Real-Time Salient Object Detection // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 3912-3921.
[14] FENG M Y, LU H C, DING E R.Attentive Feedback Network for Boundary-Aware Salient Object Detection // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 1623-1632.
[15] LEE M S, SHIN W S, HAN S W.TRACER: Extreme Attention Guided Salient Object Tracing Network(Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 2022, 36(11): 12993-12994.
[16] LIU J J, HOU Q B, LIU Z A, et al. PoolNet+: Exploring the Potential of Pooling for Salient Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(1): 887-904.
[17] XIE C X, XIA C Q, MA M C, et al. Pyramid Grafting Network for One-Stage High Resolution Saliency Detection // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2022: 11707-11716.
[18] FANG C W, TIAN H B, ZHANG D W, et al. Densely Nested Top-Down Flows for Salient Object Detection. Science China Information Sciences, 2022, 65(8). DOI: 10.1007/s11432-021-3384-y.
[19] ZHAO J X, LIU J J, FAN D P, et al. EGNet: Edge Guidance Network for Salient Object Detection // Proc of the IEEE/CVF International Conference on Computer Vision. Washington, USA: IEEE, 2019: 8778-8787.
[20] WANG L J, LU H C, WANG Y F, et al. Learning to Detect Sa-lient Objects with Image-Level Supervision // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2017: 3796-3805.
[21] YAN Q, XU L, SHI J P, et al. Hierarchical Saliency Detection // Proc of the IEEE Conference on Computer Vision and Pattern Re-cognition. Washington, USA: IEEE, 2013: 1155-1162.
[22] LI Y, HOU X D, KOCH C, et al. The Secrets of Salient Object Segmentation // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2014: 280-287.
[23] MOVAHEDI V, ELDER J H.Design and Perceptual Validation of Performance Measures for Salient Object Segmentation // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops. Washington, USA: IEEE, 2010: 49-56.
[24] YANG C, ZHANG L H, LU H C, et al. Saliency Detection via Graph-Based Manifold Ranking // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2013: 3166-3173.
[25] HE K M, ZHAG X Y, REN S Q, et al. Deep Residual Learning for Image Recognition // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 770-778.
[26] TAN M X, LE Q V.EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks // Proc of the 36th International Conference on Machine Learning. San Diego, USA: JMLR, 2019: 6105-6114.
[27] DING X H, GUO Y C, DING G G, et al. ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks // Proc of the IEEE/CVF International Conference on Computer Vision. Washington, USA: IEEE, 2019: 1911-1920.
[28] WEI J, WANG S H, HUANG Q M.F³Net: Fusion, Feedback and Focus for Salient Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7): 12321-12328.
[29] ACHANTA R, HEMAMI S, ESTRADA F, et al. Frequency-Tuned Salient Region Detection // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2009: 1597-1604.
[30] FAN D P, CHENG M M, LIU Y, et al. Structure-Measure: A New Way to Evaluate Foreground Maps // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2017: 4558-4567.
[31] SHEN Z Q, SAVVIDES M.MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks[C/OL]. [2022-09-15].https://arxiv.org/pdf/2009.08453.pdf.
[32] RUSSAKOVSKY O, DENG J, SU H, et al. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 2015, 115(3): 211-252.
[33] KINGMA D P, BA J L. Adam: A Method for Stochastic Optimization[C/OL]. [2022-09-15]. https://arxiv.org/pdf/1412.6980.pdf.
[34] ZHOU H J, XIE X H, LAI J H, et al. Interactive Two-Stream Decoder for Accurate and Fast Saliency Detection // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2020: 9138-9147.
[35] PANG Y W, ZHAO X Q, ZHANG L H, et al. Multi-scale Interactive Network for Salient Object Detection // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2020: 9413-9422.
[36] LIU N, HAN J W, YANG M H.PiCANet: Learning Pixel-Wise Contextual Attention for Saliency Detection // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2018: 3089-3098.
[37] QIN X B, ZHANG Z C, HUANG C Y, et al. U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection. Pattern Recognition, 2020, 106. DOI: 10.1016/j.patcog.2020.107404.
[38] ZHANG L, ZHANG J M, LIN Z, et al. CapSal: Leveraging Captioning to Boost Semantics for Salient Object Detection // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 6017-6026.
[39] WANG T T, ZHANG L H, WANG S, et al. Detect Globally, Refine Locally: A Novel Approach to Saliency Detection // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2018: 3127-3135.