Features Split and Aggregation Network for Camouflaged Object Detection

Higher standards have been proposed for detection systems since camouflaged objects are not distinct enough, making it possible to ignore the difference between their background and foreground. In this paper, we present a new framework for Camouflaged Object Detection (COD) named FSANet, which consi...
Ausführliche Beschreibung

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Autor*in:	Zejin Zhang [verfasserIn] Tao Wang [verfasserIn] Jian Wang [verfasserIn] Yao Sun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	bio-inspired network context-aware features multi-scale features camouflaged object detection

Übergeordnetes Werk:	In: Journal of Imaging - MDPI AG, 2016, 10(2024), 1, p 24
Übergeordnetes Werk:	volume:10 ; year:2024 ; number:1, p 24

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/jimaging10010024

Katalog-ID:	DOAJ096334266

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callnumber-first	T - Technology
author	Zejin Zhang
spellingShingle	Zejin Zhang misc TR1-1050 misc R858-859.7 misc QA75.5-76.95 misc bio-inspired network misc context-aware features misc multi-scale features misc camouflaged object detection misc Photography misc Computer applications to medicine. Medical informatics misc Electronic computers. Computer science Features Split and Aggregation Network for Camouflaged Object Detection
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topic_title	TR1-1050 R858-859.7 QA75.5-76.95 Features Split and Aggregation Network for Camouflaged Object Detection bio-inspired network context-aware features multi-scale features camouflaged object detection
topic	misc TR1-1050 misc R858-859.7 misc QA75.5-76.95 misc bio-inspired network misc context-aware features misc multi-scale features misc camouflaged object detection misc Photography misc Computer applications to medicine. Medical informatics misc Electronic computers. Computer science
topic_unstemmed	misc TR1-1050 misc R858-859.7 misc QA75.5-76.95 misc bio-inspired network misc context-aware features misc multi-scale features misc camouflaged object detection misc Photography misc Computer applications to medicine. Medical informatics misc Electronic computers. Computer science
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title	Features Split and Aggregation Network for Camouflaged Object Detection
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title_full	Features Split and Aggregation Network for Camouflaged Object Detection
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title_sort	features split and aggregation network for camouflaged object detection
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title_auth	Features Split and Aggregation Network for Camouflaged Object Detection
abstract	Higher standards have been proposed for detection systems since camouflaged objects are not distinct enough, making it possible to ignore the difference between their background and foreground. In this paper, we present a new framework for Camouflaged Object Detection (COD) named FSANet, which consists mainly of three operations: spatial detail mining (SDM), cross-scale feature combination (CFC), and hierarchical feature aggregation decoder (HFAD). The framework simulates the three-stage detection process of the human visual mechanism when observing a camouflaged scene. Specifically, we have extracted five feature layers using the backbone and divided them into two parts with the second layer as the boundary. The SDM module simulates the human cursory inspection of the camouflaged objects to gather spatial details (such as edge, texture, etc.) and fuses the features to create a cursory impression. The CFC module is used to observe high-level features from various viewing angles and extracts the same features by thoroughly filtering features of various levels. We also design side-join multiplication in the CFC module to avoid detail distortion and use feature element-wise multiplication to filter out noise. Finally, we construct an HFAD module to deeply mine effective features from these two stages, direct the fusion of low-level features using high-level semantic knowledge, and improve the camouflage map using hierarchical cascade technology. Compared to the nineteen deep-learning-based methods in terms of seven widely used metrics, our proposed framework has clear advantages on four public COD datasets, demonstrating the effectiveness and superiority of our model.
abstractGer	Higher standards have been proposed for detection systems since camouflaged objects are not distinct enough, making it possible to ignore the difference between their background and foreground. In this paper, we present a new framework for Camouflaged Object Detection (COD) named FSANet, which consists mainly of three operations: spatial detail mining (SDM), cross-scale feature combination (CFC), and hierarchical feature aggregation decoder (HFAD). The framework simulates the three-stage detection process of the human visual mechanism when observing a camouflaged scene. Specifically, we have extracted five feature layers using the backbone and divided them into two parts with the second layer as the boundary. The SDM module simulates the human cursory inspection of the camouflaged objects to gather spatial details (such as edge, texture, etc.) and fuses the features to create a cursory impression. The CFC module is used to observe high-level features from various viewing angles and extracts the same features by thoroughly filtering features of various levels. We also design side-join multiplication in the CFC module to avoid detail distortion and use feature element-wise multiplication to filter out noise. Finally, we construct an HFAD module to deeply mine effective features from these two stages, direct the fusion of low-level features using high-level semantic knowledge, and improve the camouflage map using hierarchical cascade technology. Compared to the nineteen deep-learning-based methods in terms of seven widely used metrics, our proposed framework has clear advantages on four public COD datasets, demonstrating the effectiveness and superiority of our model.
abstract_unstemmed	Higher standards have been proposed for detection systems since camouflaged objects are not distinct enough, making it possible to ignore the difference between their background and foreground. In this paper, we present a new framework for Camouflaged Object Detection (COD) named FSANet, which consists mainly of three operations: spatial detail mining (SDM), cross-scale feature combination (CFC), and hierarchical feature aggregation decoder (HFAD). The framework simulates the three-stage detection process of the human visual mechanism when observing a camouflaged scene. Specifically, we have extracted five feature layers using the backbone and divided them into two parts with the second layer as the boundary. The SDM module simulates the human cursory inspection of the camouflaged objects to gather spatial details (such as edge, texture, etc.) and fuses the features to create a cursory impression. The CFC module is used to observe high-level features from various viewing angles and extracts the same features by thoroughly filtering features of various levels. We also design side-join multiplication in the CFC module to avoid detail distortion and use feature element-wise multiplication to filter out noise. Finally, we construct an HFAD module to deeply mine effective features from these two stages, direct the fusion of low-level features using high-level semantic knowledge, and improve the camouflage map using hierarchical cascade technology. Compared to the nineteen deep-learning-based methods in terms of seven widely used metrics, our proposed framework has clear advantages on four public COD datasets, demonstrating the effectiveness and superiority of our model.
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container_issue	1, p 24
title_short	Features Split and Aggregation Network for Camouflaged Object Detection
url	https://doi.org/10.3390/jimaging10010024 https://doaj.org/article/4522f2c2f0ee435fb10c3755bd504000 https://www.mdpi.com/2313-433X/10/1/24 https://doaj.org/toc/2313-433X
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author2	Tao Wang Jian Wang Yao Sun
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up_date	2024-07-03T19:35:31.766Z
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