August 17, 2025IET Image ProcessingOpen Access

PAP‐YOLOv8: Hierarchical Feature Optimization With Dual‐Stage Attention for Object Detection in Remote Sensing Scenes

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Authors

PZPengfei ZhangChinese Academy of Sciences JLJian LiuNanjing Normal University JZJianqiang ZhangQilu University of Technology

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Implication

PAP-YOLOv8 demonstrates improved detection capabilities in remote sensing, indicating advancements in small target identification and feature optimization.

Key Points

PAP-YOLOv8 achieved an mAP@0.5 of 86.9%, boosting detection rates by 5.3% compared to YOLOv8.
This improved architecture enhances detection for small and multi-scale targets, crucial for remote sensing applications.
Observational analysis using the DIOR dataset highlights the method's superior performance in complex environments.
The study suggests PAP-YOLOv8 could significantly aid satellite monitoring, balancing network efficiency and detection accuracy.

Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/68a36f840a429f797333237e https://doi.org/10.1049/ipr2.70177

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Implication

PAP-YOLOv8 demonstrates improved detection capabilities in remote sensing, indicating advancements in small target identification and feature optimization.

PAP‐YOLOv8: Hierarchical Feature Optimization With Dual‐Stage Attention for Object Detection in Remote Sensing Scenes

Discussion

Member takes

Implication

Key Points

Cite This Study

PAP‐YOLOv8: Hierarchical Feature Optimization With Dual‐Stage Attention for Object Detection in Remote Sensing Scenes

Discussion

Member takes

Implication

Key Points

Cite This Study

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