An Attention Mechanism Based AVOD Network for 3D Vehicle Detection

Yuan, D; Zhang, H; Shu, X; Liu, Q; Chang, X; He, Z; Shi, G

An Attention Mechanism Based AVOD Network for 3D Vehicle Detection

Yuan, D Zhang, H Shu, X Liu, Q Chang, X

He, Z Shi, G

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Intelligent Vehicles, 2023, PP, (99), pp. 1-13
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Yuan, D
dc.contributor.author	Zhang, H
dc.contributor.author	Shu, X
dc.contributor.author	Liu, Q
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807
dc.contributor.author	He, Z
dc.contributor.author	Shi, G
dc.date.accessioned	2024-03-27T07:04:29Z
dc.date.available	2024-03-27T07:04:29Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Intelligent Vehicles, 2023, PP, (99), pp. 1-13
dc.identifier.issn	2379-8858
dc.identifier.issn	2379-8858
dc.identifier.uri	http://hdl.handle.net/10453/177279
dc.description.abstract	With the continuous advancement of autonomous driving technology, 3D vehicle detection has become of widespread interest. The traditional aggregate view object detection (AVOD) framework has achieved some good results in 3D vehicle detection tasks. However, the complexity of the 3D vehicle detection scenario makes the current detection methods still not meet the actual requirements. To enhance the detection accuracy of 3D vehicle targets, we propose to equip an attention mechanism to improve the representation capability of feature maps, thereby further increasing the precision of 3D vehicle detection. Specifically, we have added the channel attention ECANet, spatial attention SANet, and mixed attention ECANet+SANet respectively into the image-based feature pyramid network of the AVOD detection framework, which can enhance the feature maps representation and improve the detection accuracy observably. The improved AVOD network is verified using the KITTI dataset. By showing the detection results of these attention mechanisms, it is found that the feature pyramid networks (FPN) module in the AVOD network based on Image has the best performance when integrating a mixed attention mechanism. In comparison to the original AVOD network, the detection results on the average precision index of the proposed method have improved by 2.29%, 2.81%, and 1.32% in the three indexes of simple, medium, and difficult, respectively. Extensive experiments have confirmed the practicality and efficacy of the AVOD network to equip the attention mechanisms for 3D vehicle detection.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Intelligent Vehicles
dc.relation.isbasedon	10.1109/TIV.2023.3323960
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	4002 Automotive engineering
dc.subject.classification	4007 Control engineering, mechatronics and robotics
dc.subject.classification	4603 Computer vision and multimedia computation
dc.title	An Attention Mechanism Based AVOD Network for 3D Vehicle Detection
dc.type	Journal Article
utslib.citation.volume	PP
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 - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-27T07:04:08Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

With the continuous advancement of autonomous driving technology, 3D vehicle detection has become of widespread interest. The traditional aggregate view object detection (AVOD) framework has achieved some good results in 3D vehicle detection tasks. However, the complexity of the 3D vehicle detection scenario makes the current detection methods still not meet the actual requirements. To enhance the detection accuracy of 3D vehicle targets, we propose to equip an attention mechanism to improve the representation capability of feature maps, thereby further increasing the precision of 3D vehicle detection. Specifically, we have added the channel attention ECANet, spatial attention SANet, and mixed attention ECANet+SANet respectively into the image-based feature pyramid network of the AVOD detection framework, which can enhance the feature maps representation and improve the detection accuracy observably. The improved AVOD network is verified using the KITTI dataset. By showing the detection results of these attention mechanisms, it is found that the feature pyramid networks (FPN) module in the AVOD network based on Image has the best performance when integrating a mixed attention mechanism. In comparison to the original AVOD network, the detection results on the average precision index of the proposed method have improved by 2.29%, 2.81%, and 1.32% in the three indexes of simple, medium, and difficult, respectively. Extensive experiments have confirmed the practicality and efficacy of the AVOD network to equip the attention mechanisms for 3D vehicle detection.

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http://hdl.handle.net/10453/177279