SpatialIE: Towards adaptive floating waste detection in unpredictable weather

Yin, Y; Liu, X; Cheng, X; Liu, Y; Zhu, T; Huo, H

SpatialIE: Towards adaptive floating waste detection in unpredictable weather

Yin, Y Liu, X Cheng, X Liu, Y Zhu, T Huo, H

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Knowledge Based Systems, 2025, 323, pp. 113621
Issue Date:: 2025-07-19

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Field	Value	Language
dc.contributor.author	Yin, Y
dc.contributor.author	Liu, X
dc.contributor.author	Cheng, X
dc.contributor.author	Liu, Y
dc.contributor.author	Zhu, T
dc.contributor.author	Huo, H https://orcid.org/0000-0003-2440-714X
dc.date.accessioned	2025-10-15T03:55:44Z
dc.date.available	2025-10-15T03:55:44Z
dc.date.issued	2025-07-19
dc.identifier.citation	Knowledge Based Systems, 2025, 323, pp. 113621
dc.identifier.issn	0950-7051
dc.identifier.uri	http://hdl.handle.net/10453/190423
dc.description.abstract	Accurate detection and subsequent cleanup of floating waste are critical for ecosystem protection. However, existing methods face significant challenges in dealing with unpredictable weather, limiting their generalization capabilities. This paper proposes a novel plug-and-play architecture called Image Enhancer with Spatial Search and Aggregation (SpatialIE). This architecture includes a novel encoder block (KANsformer) based on KANs and a dynamic decoder module leveraging prompt-based learning. The KANsformer in the encoder enhances the model's ability to learn complex non-linear relationships in degraded images. At the same time, the Multi-Order-Based Prompt Block in the decoder enables dynamic optimization of feature processing strategies. These components allow SpatialIE to refine detection strategies to accommodate unknown weather conditions adaptively. We integrate SpatialIE with common YOLO detectors in an end-to-end training framework using the FloW-img and Water Surface Object Detection Dataset (WSODD) datasets. Experimental results show that our method delivers outstanding performance across multiple and single degradation scenarios, achieving an optimal balance between detection accuracy and storage efficiency.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Knowledge Based Systems
dc.relation.isbasedon	10.1016/j.knosys.2025.113621
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 15 Commerce, Management, Tourism and Services, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4605 Data management and data science
dc.subject.classification	4611 Machine learning
dc.title	SpatialIE: Towards adaptive floating waste detection in unpredictable weather
dc.type	Journal Article
utslib.citation.volume	323
utslib.for	08 Information and Computing Sciences
utslib.for	15 Commerce, Management, Tourism and Services
utslib.for	17 Psychology and Cognitive Sciences
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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Cyber Security and Privacy (CCSP)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-10-15T03:55:38Z
pubs.publication-status	Published
pubs.volume	323

Abstract:

Accurate detection and subsequent cleanup of floating waste are critical for ecosystem protection. However, existing methods face significant challenges in dealing with unpredictable weather, limiting their generalization capabilities. This paper proposes a novel plug-and-play architecture called Image Enhancer with Spatial Search and Aggregation (SpatialIE). This architecture includes a novel encoder block (KANsformer) based on KANs and a dynamic decoder module leveraging prompt-based learning. The KANsformer in the encoder enhances the model's ability to learn complex non-linear relationships in degraded images. At the same time, the Multi-Order-Based Prompt Block in the decoder enables dynamic optimization of feature processing strategies. These components allow SpatialIE to refine detection strategies to accommodate unknown weather conditions adaptively. We integrate SpatialIE with common YOLO detectors in an end-to-end training framework using the FloW-img and Water Surface Object Detection Dataset (WSODD) datasets. Experimental results show that our method delivers outstanding performance across multiple and single degradation scenarios, achieving an optimal balance between detection accuracy and storage efficiency.

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http://hdl.handle.net/10453/190423