Object Detection Method Based on Shallow Feature Fusion and Semantic Information Enhancement

Luo, H; Wang, P; Chen, H; Xu, M

Object Detection Method Based on Shallow Feature Fusion and Semantic Information Enhancement

Luo, H Wang, P Chen, H Xu, M

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Sensors Journal, 2021, 21, (19), pp. 21839-21851
Issue Date:: 2021-10-01

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Field	Value	Language
dc.contributor.author	Luo, H
dc.contributor.author	Wang, P
dc.contributor.author	Chen, H
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849
dc.date.accessioned	2022-05-18T22:20:04Z
dc.date.available	2022-05-18T22:20:04Z
dc.date.issued	2021-10-01
dc.identifier.citation	IEEE Sensors Journal, 2021, 21, (19), pp. 21839-21851
dc.identifier.issn	1530-437X
dc.identifier.issn	1558-1748
dc.identifier.uri	http://hdl.handle.net/10453/157503
dc.description.abstract	In recent years, object detection algorithm based on deep learning has made great progress, but the detection effect is not ideal for small objects detection. Some methods use high-resolution features or enhance shallow features to improve the detection accuracy of small objects. However, using high-resolution features for detection needs higher computational cost, and enhancing shallow features by propagating semantic information from high-level into low-level may bring information aliasing. To address this issue, we propose a novel object detection method based on shallow feature fusion and semantic information enhancement (FFSI). The high-level semantic information is injected into low-level features to guide the enhancement of specific detail information. In order to reduce the information aliasing in shallow features and enhance the receptive field of shallow features, we design two parallel modules: context information enhancement module (CIE) and receptive field enhancement module (RFE). CIE highlights the location of objects by establishing the relationship between local and global context information. RFE enhances the receptive field of shallow features by using dilated convolution to adapt to object detection of different scales, especially small objects. The proposed model is evaluated extensively on PASCAL VOC, and COCO datasets. The experimental results demonstrate that the proposed FFSI model has competitive performance. More importantly, this study reveals that FFSI outperforms the state-of-the-art methods in detecting small objects.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Sensors Journal
dc.relation.isbasedon	10.1109/JSEN.2021.3103612
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0205 Optical Physics, 0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Analytical Chemistry
dc.title	Object Detection Method Based on Shallow Feature Fusion and Semantic Information Enhancement
dc.type	Journal Article
utslib.citation.volume	21
utslib.for	0205 Optical Physics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-05-18T22:19:54Z
pubs.issue	19
pubs.publication-status	Published
pubs.volume	21
utslib.citation.issue	19

Abstract:

In recent years, object detection algorithm based on deep learning has made great progress, but the detection effect is not ideal for small objects detection. Some methods use high-resolution features or enhance shallow features to improve the detection accuracy of small objects. However, using high-resolution features for detection needs higher computational cost, and enhancing shallow features by propagating semantic information from high-level into low-level may bring information aliasing. To address this issue, we propose a novel object detection method based on shallow feature fusion and semantic information enhancement (FFSI). The high-level semantic information is injected into low-level features to guide the enhancement of specific detail information. In order to reduce the information aliasing in shallow features and enhance the receptive field of shallow features, we design two parallel modules: context information enhancement module (CIE) and receptive field enhancement module (RFE). CIE highlights the location of objects by establishing the relationship between local and global context information. RFE enhances the receptive field of shallow features by using dilated convolution to adapt to object detection of different scales, especially small objects. The proposed model is evaluated extensively on PASCAL VOC, and COCO datasets. The experimental results demonstrate that the proposed FFSI model has competitive performance. More importantly, this study reveals that FFSI outperforms the state-of-the-art methods in detecting small objects.

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http://hdl.handle.net/10453/157503