Abstract
Existing conveyor belt monitoring methods operating under complex conditions generally suffer from severe environmental background interference, feature conflicts between multi-scale targets, and insufficient directional boundary perception capabilities. To address these issues, this study proposes an omni-directional gated spatial attention network (OGSA-YOLO), which establishes a cohesive and unified architecture to systematically decouple multi-scale feature variations and suppress non-structural environmental noise. The framework operates as a progressive feature-refinement pipeline: first, an omni-directional perception strategy (combining ODP-Conv and GSDF-Block) collaboratively separates high-frequency directional details from macroscopic semantics. Subsequently, a structural prior-based attention mechanism (SCSAB) proactively filters out intense ambient dust while preserving fragile target boundaries. Finally, an adaptive feature fusion module (AMFAM) dynamically aligns these refined cross-scale features to resolve semantic conflicts. We conduct extensive experiments on the conveyor belt monitoring dataset and the Cityscapes dataset. On the conveyor belt dataset, the \(\textrm{mAP}_{50}\) and \(\textrm{mAP}_{95}\) for the detection task reach 96.9% and 84.8%, while the \(\textrm{mAP}_{50}\) and \(\textrm{mAP}_{95}\) for the segmentation task reach 96.1% and 77.4%, respectively. These results comprehensively outperform most state-of-the-art methods.
Data availability
No datasets were generated or analyzed during the current study.
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Kelei Sun contributed to supervision, investigation, and funding acquisition; Xuedong Feng contributed to writing—conceptualization, methodology, software, and writing—original draft; Huaping Zhou contributed to writing—review & editing; Tao Wu contributed to software; Bin Deng contributed to visualization.
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Sun, K., Feng, X., Zhou, H. et al. OGSA-YOLO: Omni-directional Gated Spatial-Attention Network for Conveyor Belt Monitoring in Complex Environments. J Supercomput 82, 706 (2026). https://doi.org/10.1007/s11227-026-08817-7
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DOI: https://doi.org/10.1007/s11227-026-08817-7