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Salient Object Detection Based on Pyramid Features and Edge Optimization
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[1]TIAN Xu,PENG Fei,LIU Fei,et al.Salient Object Detection Based on Pyramid Features and Edge Optimization[J].Journal of Zhengzhou University (Engineering Science),2022,43(02):35-43.[doi:10.13705/j.issn.1671-6833.2022.02.003]
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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43卷 Number of periods: 2022 02 Page number: 35-43 Column: Public date: 2022-02-27
Title:
Salient Object Detection Based on Pyramid Features and Edge Optimization
Author(s):
TIAN Xu1;  PENG Fei1;  LIU Fei1;  CHEN Qingwen2;  YAN Xinyu3; 4
1.State Grid Qinghai Electric Power Company Economic and Technical Research Institute, Xining 810000, China;
2.Northwest Engineering Corporation Limited, Xi′an 710065, China; 
3.College of Intelligence and Computing, Tianjin University, Tianjin 300350, China;
4.Tianjin Key Lab of Machine Learning, Tianjin University, Tianjin 300350, China
Keywords:
salient object detection;  multi-scale feature extraction;  fully convolutional networks;  edge feature extraction;  deep learning
CLC:
TP391.4
DOI:
10.13705/j.issn.1671-6833.2022.02.003
Abstract:
To solve the problems of insufficient multiscale feature extraction and object edge blur in image-based salient object detection, an end-to-end salient object detection model was proposed based on attention embedding pyramid feature and stepped edge optimization. Firstly, the attention embedded dense atrous Pyramid Module (AEDAPM) composed of multiple dilated convolutions was designed to obtain rich and effective multi-level multi-scale features without reducing the feature resolution； Secondly, in order to solve the problem of blurring the edges of salient objects, a stepped edge optimization module (SEOM) is proposed, which gradually supplements spatial detail information during the process of feature restoration resolution, so that the salient objects detected by the model could have clear edge contours. The method in this paper was compared with 12 state-of-the-art saliency methods under 3 common indicators on 5 public datasets, such as DUTS-TE, ECSSD, DUT-OMRON, HKU-IS, and PASCAL-S. The experimental results show that the method proposed in this paper can obtain more accurate and clearer saliency results. In addition, the ablation study also fully proved the effectiveness of the AEDAPM and the SEOM proposed in this study.
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Last Update: 2022-02-25
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