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

ABSTRACT Remote sensing image target recognition plays an important role in environmental monitoring, disaster management, urban planning, and traffic systems. To address issues such as high missing detection rates of dense small targets and insufficient multi‐scale feature interaction in remote sensing image target detection, an improved PAP‐YOLOv8 network architecture was proposed in this study based on YOLOv8. This framework included three core innovations: (1) the pixel dynamic convolution attention module (PDCA), which enhanced small target detection capabilities in complex backgrounds through direction‐sensitive convolution and a dual‐stage attention mechanism; (2) the adaptive channel attention network (ACAN), which optimized multi‐scale feature fusion and suppressed background interference via dynamic weight allocation and channel recalibration; (3) the pyramidal wavelet attention fusion block (PWAFB), which synergistically integrated wavelet multi‐resolution analysis with composite attention mechanisms to model spatial details and channel relationships in remote sensing images through a collaborative decomposition and reconstruction workflow. Experiments on the Detection in Optical Remote Sensing (DIOR) dataset showed that the [email protected] of PAP‐YOLOv8 reached 86.9%, an increase of 5.3% over the baseline YOLOv8, while demonstrating enhanced detection capability for small and multi‐scale targets. Compared with other detection models, PAP‐YOLOv8 achieves a balanced trade‐off between network size and detection capability, demonstrating superior overall performance with an F1 score of 83.1%. This study confirms the potential of PAP‐YOLOv8 for satellite monitoring applications and provides a valuable reference for improving target detection performance in optical remote sensing satellite imagery.