Scale Decoupled Pyramid for Object Detection in Aerial Images

Object detection in aerial images is a challenging task for two main reasons: small object and scale variation. Existing methods utilize multi-level features to solve the scale variation problem, but ignore the scale confusion problem of shallow features, limiting the small object detection performance. To solve this issue, we propose a scale decoupling module to emphasizes small object features by eliminating large object features in shallow layers. Moreover, a sparse non-local attention (SNLA) and an adaptive anchor matching strategy (AAMS) are proposed to further improve the small object detection performance. The SNLA only aggregates contextual information of specific sparse positions, which not only refines small object features but also is computationally friendly. The AAMS is suitable for the measurement of small objects, and it can assign more positive samples to small objects. Extensive experiments on 3 challenging aerial datasets, VisDrone-DET2019, UAVDT and DIOR, demonstrate the effectiveness and adaptivity of our method. Code will be available online (https://github.com/MaYou1997/SDP).