Multistrengthening Module-Based Salient Object Detection
Author(s) -
Qian Zhao,
Haifeng Wang,
Junpeng Dang,
Songlin Li,
RongChi Chang,
Yanbin Fang,
Zhi Zhang,
Jie Peng,
Yang Yang
Publication year - 2021
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2021/2472676
Subject(s) - salient , object detection , computer science , object (grammar) , field (mathematics) , artificial intelligence , state (computer science) , computer vision , image (mathematics) , function (biology) , pattern recognition (psychology) , algorithm , mathematics , evolutionary biology , pure mathematics , biology
Object detection is a classical research problem in computer vision, and it is widely used in the automatic monitoring field of various production safety. However, current object detection techniques often suffer low detection accuracy when an image has a complex background. To solve this problem, this paper proposes a double U-shaped multireinforced unit structure network (DUMRN). The proposed network consists of a detection module (DM), a reinforced module (RM), and a salient loss function (SLF). Extensive experiments on five public datasets and a practical application dataset are conducted and compared against nine state-of-the-art methods. The experiment results show the superiority of our method over the state of the art.
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