Skipped Feature Pyramid Network with Grid Anchor for Object Detection
Li Pengfei Wei Wei Yan Yu Zhu Rong Zhou Liguo
CNN-based object detection methods have achieved significant progress in
recent years. The classic structures of CNNs produce pyramid-like feature maps
due to the pooling or other re-scale operations. The feature maps in different
levels of the feature pyramid are used to detect objects with different scales.
For more accurate object detection, the highest-level feature, which has the
lowest resolution and contains the strongest semantics, is up-scaled and
connected with the lower-level features to enhance the semantics in the
lower-level features. However, the classic mode of feature connection combines
the feature of lower-level with all the features above it, which may result in
semantics degradation. In this paper, we propose a skipped connection to obtain
stronger semantics at each level of the feature pyramid. In our method, the
lower-level feature only connects with the feature at the highest level, making
it more reasonable that each level is responsible for detecting objects with
fixed scales. In addition, we simplify the generation of anchor for bounding
box regression, which can further improve the accuracy of object detection. The
experiments on the MS COCO and Wider Face demonstrate that our method
outperforms the state-of-the-art methods.