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International Journal of Machine Learning and Cybernetics

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09.09.2023 | Original Article

PaIaNet: position-aware and identification-aware network for low-light salient object detection

verfasst von: Huihui Yue, Jichang Guo, Xiangjun Yin, Yi Zhang, Sida Zheng

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 3/2024

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Abstract

Due to insufficient photons and undesirable noise, salient objects in low-light scenes are ambiguous, thus limiting the performance of existing salient object detection (SOD) works. To solve this problem, inspired by the hunting mechanism of predators in biology, we propose a position-aware and identification-aware network (PaIaNet) for SOD. First, we design a position-aware decoder (PaD) for obtaining position encodes by locating the edges and main bodies of salient objects. Second, we construct an identification-aware decoder (IaD) to reason accurate saliency maps by aggregating adjacent features under the guidance of position encodes. Moreover, we propose a reverse loss to suppress background interference effectively. Extensive experiments demonstrate that our method performs favorably from comparisons of qualitative and quantitative evaluations against other state-of-the-art methods in SOD of low-light images, and even achieves competitive performance when extended to normal-light scenes. Code will be available at https://github.com/yuehuihui000/PaIaNet.

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Titel: PaIaNet: position-aware and identification-aware network for low-light salient object detection
verfasst von: Huihui Yue
Jichang Guo
Xiangjun Yin
Yi Zhang
Sida Zheng
Publikationsdatum: 09.09.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: International Journal of Machine Learning and Cybernetics / Ausgabe 3/2024
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI: https://doi.org/10.1007/s13042-023-01960-0

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