Spherical Geometry-Guided and Frequency-Enhanced Segment Anything Model for 360° Salient Object Detection

CHEN Xiaolei; SHEN Yujie; ZHONG Zhihua

doi:10.11999/JEIT251254

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CHEN Xiaolei, SHEN Yujie, ZHONG Zhihua. Spherical Geometry-Guided and Frequency-Enhanced Segment Anything Model for 360° Salient Object Detection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251254

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CHEN Xiaolei, SHEN Yujie, ZHONG Zhihua. Spherical Geometry-Guided and Frequency-Enhanced Segment Anything Model for 360° Salient Object Detection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251254

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Spherical Geometry-Guided and Frequency-Enhanced Segment Anything Model for 360° Salient Object Detection

doi: 10.11999/JEIT251254 cstr: 32379.14.JEIT251254

School of Microelectronics Industry-education Integration, Lanzhou University of Technology, Lanzhou 730050, China

Funds: Item1, Item2, Item3

Accepted Date: 2026-01-26
Rev Recd Date: 2026-01-26

Available Online: 2026-02-12

Abstract

Abstract

Objective With the rapid development of VR and AR technologies and the increasing demand for omnidirectional visual applications, accurate salient object detection in complex 360° scenes has become critical for system stability and intelligent decision-making. The Segment Anything Model (SAM) demonstrates strong transferability across 2D vision tasks; however, it is primarily designed for planar images and lacks explicit modeling of spherical geometry, which limits its direct applicability to 360° salient object detection (360° SOD). To address this challenge, this work explores integrating SAM’s generalization capability with spherical-aware multi-scale geometric modeling to advance 360° SOD. Specifically, a Multi-Cognitive Adapter (MCA), Spherical Geometry-Guided Attention (SGGA), and Spatial-Frequency Joint Perception Module (SFJPM) are introduced to enhance multi-scale structural representation, alleviate projection-induced geometric distortions and boundary discontinuities, and strengthen joint global–local feature modeling. Methods The proposed 360° SOD framework is built upon SAM and consists of an image encoder and a mask decoder. During encoding, spherical geometry modeling is incorporated into patch embedding by mapping image patches onto a unit sphere and explicitly modeling spatial relationships between patch centers, injecting geometric priors into the attention mechanism. This design enhances sensitivity to non-uniform geometric characteristics and mitigates information loss caused by omnidirectional projection distortions. The encoder adopts a partial freezing strategy and is organized into four stages, each containing three encoder blocks. Each block integrates MCA for multi-scale contextual fusion and SGGA to model long-range dependencies in spherical space. Multi-level features are concatenated along the channel dimension to form a unified representation, which is further enhanced by the SFJPM to jointly capture spatial structures and frequency-domain global information. The fused features are then fed into the SAM mask decoder, where saliency maps are optimized under ground-truth supervision to achieve accurate localization and boundary refinement. Results and Discussions Experiments are conducted using the PyTorch framework on an RTX 3090 GPU with an input resolution of 512×512. Evaluations on two public datasets (360-SOD and 360-SSOD) against 14 state-of-the-art methods demonstrate that the proposed approach consistently achieves superior performance across six evaluation metrics. On the 360-SOD dataset, the model attains an MAE of 0.0152 and a maximum F-measure of 0.8492, outperforming representative methods such as MDSAM and DPNet. Qualitative results show that the proposed method produces saliency maps highly consistent with ground-truth annotations, effectively handling challenging scenarios including projection distortion, boundary discontinuity, multi-object scenes, and complex backgrounds. Ablation studies further confirm that MCA, SGGA, and SFJPM contribute independently while complementing each other to improve detection performance. Conclusions This paper proposes a novel SAM-based framework for 360° salient object detection that jointly addresses multi-scale representation, spherical distortion awareness, and spatial-frequency feature modeling. The MCA enables efficient multi-scale feature fusion, SGGA explicitly compensates for ERP-induced geometric distortions, and SFJPM enhances long-range dependency modeling. Extensive experiments validate the effectiveness and feasibility of introducing SAM into 360° SOD. Future work will extend this framework to omnidirectional video and multi-modal scenarios to further improve spatiotemporal modeling and scene understanding.
- 360° Omnidirectional Images,
- Salient Object Detection,
- Segment Anything Model

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References(31)

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