Noise-Aware Intermediary Fusion Network for Off-Road Freespace Detection

Lv, Y; Liu, Z; Li, G; Chang, X

Noise-Aware Intermediary Fusion Network for Off-Road Freespace Detection

Lv, Y Liu, Z Li, G Chang, X

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Intelligent Vehicles, 2024, PP, (99), pp. 1-11
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Lv, Y
dc.contributor.author	Liu, Z
dc.contributor.author	Li, G
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807
dc.date.accessioned	2024-08-07T05:29:04Z
dc.date.available	2024-08-07T05:29:04Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Intelligent Vehicles, 2024, PP, (99), pp. 1-11
dc.identifier.issn	2379-8858
dc.identifier.issn	2379-8858
dc.identifier.uri	http://hdl.handle.net/10453/180318
dc.description.abstract	Freespace detection holds a crucial role in autonomous driving technology, particularly in unstructured offroad scenarios that present additional challenges compared to structured road environments. Multimodal fusion methods are widely recognized as effective strategies for addressing these challenges. However, current fusion methods often overlook the critical issue of noise interference in multimodal data. To address this issue, we propose a novel Noise-Aware Intermediary Fusion Network for off-road freespace detection, named NAIFNet. This framework is specifically designed to mitigate noise interference during multimodal fusion. The key component of NAIFNet is the Noise-Aware Intermediary Interaction (NAII) module, which incorporates a denoising template as an intermediary during the fusion process. The NAII module employs multimodal features as query vectors, while the key and value vectors are derived from the search region features of the denoising template. At the same time, noise-aware interaction ensures effective denoising for data in each modality. Furthermore, in the decoding phase, we introduce the Denoising-Guided Decoder (DGD). Leveraging the denoising template, this decoder achieves more precise feature restoration and effectively mitigates the impact of noise. Extensive experiments on the popular benchmark of the offroad freespace detection dataset (ORFD) demonstrate that the proposed NAIFNet achieves state-of-the-art performance.
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.2024.3396346
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	Noise-Aware Intermediary Fusion Network for Off-Road Freespace 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
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Australian Artificial Intelligence Institute (AAII)
utslib.copyright.status	closed_access	*
dc.date.updated	2024-08-07T05:29:02Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Freespace detection holds a crucial role in autonomous driving technology, particularly in unstructured offroad scenarios that present additional challenges compared to structured road environments. Multimodal fusion methods are widely recognized as effective strategies for addressing these challenges. However, current fusion methods often overlook the critical issue of noise interference in multimodal data. To address this issue, we propose a novel Noise-Aware Intermediary Fusion Network for off-road freespace detection, named NAIFNet. This framework is specifically designed to mitigate noise interference during multimodal fusion. The key component of NAIFNet is the Noise-Aware Intermediary Interaction (NAII) module, which incorporates a denoising template as an intermediary during the fusion process. The NAII module employs multimodal features as query vectors, while the key and value vectors are derived from the search region features of the denoising template. At the same time, noise-aware interaction ensures effective denoising for data in each modality. Furthermore, in the decoding phase, we introduce the Denoising-Guided Decoder (DGD). Leveraging the denoising template, this decoder achieves more precise feature restoration and effectively mitigates the impact of noise. Extensive experiments on the popular benchmark of the offroad freespace detection dataset (ORFD) demonstrate that the proposed NAIFNet achieves state-of-the-art performance.

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http://hdl.handle.net/10453/180318