sis 18: e28

Research Article

Convolutional block attention module based on visual mechanism for robot image edge detection

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  • @ARTICLE{10.4108/eai.19-11-2021.172214,
        author={Aiyun Ju and Zhongli Wang},
        title={Convolutional block attention module based on visual mechanism for robot image edge detection},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2021},
        month={11},
        keywords={deep learning, CBAM, visual mechanism, robot image edge detection},
        doi={10.4108/eai.19-11-2021.172214}
    }
    
  • Aiyun Ju
    Zhongli Wang
    Year: 2021
    Convolutional block attention module based on visual mechanism for robot image edge detection
    SIS
    EAI
    DOI: 10.4108/eai.19-11-2021.172214
Aiyun Ju1,*, Zhongli Wang2
  • 1: Department of Mechanical and Electrical Engineering, Zhengzhou Institute of Technology, Zhengzhou 451150, China
  • 2: School of Electrical Engineering, Zhengzhou University of Science and Technology, Zhengzhou 450064, China
*Contact email: publicgj@163.com

Abstract

In recent years, with the continuous development of computer vision, digital image and other information technology, its application in robot image has attracted many domestic and foreign scholars to conduct researches. Edge detection technology based on traditional deep learning produces messy and fuzzy edge lines. Therefore, we present a new convolutional block attention module (CBAM) based on visual mechanism for robot image edge detection. CBAM is added into the trunk network, and a down-sampling technique with translation invariance is adopted. Some down-sampling operations in the trunk network are removed to retain the details of the image. Meanwhile, the extended convolution technique is used to increase the model's receptive field. Training is carried out on BSDS500 and PASCAL VOL Context datasets. We use the image pyramid technique to enhance the edges quality during testing. Experimental results show that the proposed model can extract image contour more clearly than other networks, and can solve the problem of edge blur.