Dilated Strip-Wise Spatial Feature Pyramid: An Efficient Network for Object Detection

Object detection has become a fundamental capability in modern computer vision, enabling critical applications from autonomous systems to aerial surveillance. Unmanned aerial vehicle object detection (UAV-OD) presents unique challenges, including small object sizes, occlusions, and complex back-grounds. While most existing approaches rely on standard feature pyramid networks (FPN) to combine multi-level features, these methods often fail to capture long-range contextual relationships and directional patterns essential for small object detection. To address these limitations, we propose dilated strip-wise spatial feature pyramid (DSSFP), a novel architecture that explicitly focuses on long-range dependencies through dilated strip-wise convolutions (Conv), directional features via spatial-aware attention mechanisms, and multi-scale context while preserving spatial resolution. On the VisDrone dataset, our method improves average precision (AP) by +22.3% and AP50 by +26.1% compared to the baseline. Our method establishes new state-of-the-art results on the AI-TOD benchmark (27.8% AP/59.9% AP50), improving the prior work by +3.3%. The consistent gains across small, medium, and large objects demonstrate the framework's robustness for UAV applications. Source code is available at: https://github.com/harish1120/HR-DSSFP