[1]ZHAO Wenqing,YANG Panpan.Target detection based on bidirectional feature fusion and an attention mechanism[J].CAAI Transactions on Intelligent Systems,2021,16(6):1098-1105.[doi:10.11992/tis.202012029]
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Target detection based on bidirectional feature fusion and an attention mechanism

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 6 Page number: 1098-1105 Column: 学术论文—知识工程 Public date: 2021-11-05
Title:
Target detection based on bidirectional feature fusion and an attention mechanism
Author(s):
ZHAO Wenqing1; 2;  YANG Panpan1
1. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;
2. Engineering Research Center of the Ministry of education for Intelligent Computing of Complex Energy System, Baoding 071003, China
Keywords:
feature pyramid; bidirectional fusion; feature extraction; SeNet attention mechanism; sample; semantic information; target detection; deep learning
CLC:
TP391
DOI:
10.11992/tis.202012029
Abstract:
When using a feature pyramid to detect objects of different dimensions, the relationship between high- and low-level information is ignored, resulting in a poor detection effect; in addition, for targets of a certain scale, detection is easily missed. In this paper, a method combining bidirectional feature fusion and an attention mechanism is proposed for target detection. First, the deep and shallow feature layers of the single-shot multibox detector (SSD) model are fused, then the obtained features are fused with the deep feature layer. Second, the channel attention mechanism is added to the two-way fusion to enhance semantic information. Finally, an improved positive and negative sample decision strategy is proposed to reduce the target misdetection rate. The algorithm proposed in this paper is compared with the current mainstream algorithms in the VOC dataset. The results show that the average accuracy of the proposed algorithm is greatly improved when detecting targets.
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