Anchor-free Object Detection Algorithm Based on Double Branch Feature Fusion

HOU Zhiqiang; GUO Hao; MA Sugang; CHENG Huanhuan; BAI Yu; FAN Jiulun

doi:10.11999/JEIT210344

Volume 44 Issue 6

Jun. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(6): 2175-2183

HOU Zhiqiang, GUO Hao, MA Sugang, CHENG Huanhuan, BAI Yu, FAN Jiulun. Anchor-free Object Detection Algorithm Based on Double Branch Feature Fusion[J]. Journal of Electronics & Information Technology, 2022, 44(6): 2175-2183. doi: 10.11999/JEIT210344

Citation:

HOU Zhiqiang, GUO Hao, MA Sugang, CHENG Huanhuan, BAI Yu, FAN Jiulun. Anchor-free Object Detection Algorithm Based on Double Branch Feature Fusion[J]. Journal of Electronics & Information Technology, 2022, 44(6): 2175-2183. doi: 10.11999/JEIT210344

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Anchor-free Object Detection Algorithm Based on Double Branch Feature Fusion

doi: 10.11999/JEIT210344

HOU Zhiqiang^{1, 2},
GUO Hao^{1, 2
,
,},
MA Sugang^{1, 2},
CHENG Huanhuan^{1, 2},
BAI Yu^{1, 2},
FAN Jiulun¹

1.
Institute of Computer, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2.
Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an 710121, China

Funds: The National Natural Science Foundation of China (62072370)

Received Date: 2021-04-23
Accepted Date: 2022-01-12
Rev Recd Date: 2021-12-19

Available Online: 2022-02-02

Publish Date: 2022-06-21

Abstract

Abstract

Focusing on the problem of low utilization of object features and inaccurate detection results in CenterNet, an improved algorithm of double branch feature fusion is proposed in the paper. One branch of the algorithm includes feature pyramid enhancement module and feature fusion module to fuse the multi-layer features output from the backbone network. At the same time, in order to use more high-level semantic information, only the last layer of the backbone network is upsampled in the other branch. Secondly, a frequency-based channel attention mechanism is added to the backbone network to enhance feature extraction capability. Finally, the features of the two branches are concatenated and convoluted. The experimental results show that the detection accuracy on PASCAL VOC dataset is 82.3%, which is 3.6% higher than CenterNet, and the detection accuracy on KITTI dataset is 6% higher than CenterNet. The detection speed meets the real-time requirements. The double branch feature fusion method is proposed to process the features of different layers, which makes better use of the spatial information of shallow features and the semantic information of deep features, and improves the detection performance of the algorithm.
- Object detection,
- Multi-feature fusion,
- Attention mechanism,
- CenterNet

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References(13)

References

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