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Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 4/2023

28.09.2023 | Theoretical Advances

SAFPN: a full semantic feature pyramid network for object detection

verfasst von: Gaihua Wang, Qi Li, Nengyuan Wang, Hong Liu

Erschienen in: Pattern Analysis and Applications | Ausgabe 4/2023

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Abstract

To enhance the performance of object detection algorithm, this paper proposes segmentation attention feature pyramid network (SAFPN) to address the issue of semantic information loss. Compared to prior works, SAFPN discards the original \(1\times 1\) convolutions and achieves feature dimension reduction through a segmentation and accumulation architecture, thereby preserving the semantic information of high-dimensional features completely. To capture fine-grained semantic details, it integrates channel attention and spatial attention mechanisms to enhance the network’s focus on important information. Extensive experimental validation demonstrates that SAFPN achieves favorable results on multiple public datasets, and can better complete the target detection task.
Vorheriger Artikel Applying unsupervised keyphrase methods on concepts extracted from discharge sheets
Nächster Artikel Adaptive frequency-based fully hyperbolic graph neural networks

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Metadaten
Titel
SAFPN: a full semantic feature pyramid network for object detection
verfasst von
Gaihua Wang
Qi Li
Nengyuan Wang
Hong Liu
Publikationsdatum
28.09.2023
Verlag
Springer London
Erschienen in
Pattern Analysis and Applications / Ausgabe 4/2023
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-023-01200-9

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