Feature Pyramid Network (FPN) has been an essential module for object detection models to consider various scales of an object. However, average precision (AP) on small objects is relatively lower than AP on medium and large objects. The reason is why the deeper layer of CNN causes information loss as feature extraction level. We propose a new scale sequence (S^2) feature extraction of FPN to strengthen feature information of small objects. We consider FPN structure as scale-space and extract scale sequence (S^2) feature by 3D convolution on the level axis of FPN. It is basically scale invariant feature and is built on high-resolution pyramid feature map for small objects. Furthermore, the proposed S^2 feature can be extended to most object detection models based on FPN. We demonstrate the proposed S2 feature can improve the performance of both one-stage and two-stage detectors on MS COCO dataset. Based on the proposed S2 feature, we achieve upto 1.3% and 1.1% of AP improvement for YOLOv4-P5 and YOLOv4-P6, respectively. For Faster RCNN and Mask R-CNN, we observe upto 2.0% and 1.6% of AP improvement with the suggested S^2 feature, respectively.

翻译：微小物体的平均精确度(AP)相对低于中大物体的高级金字塔特征图。此外,拟议的S%2特性可以扩大到基于FPN的大多数物体探测模型。我们展示了拟议的S2特性可以改进MS COCO数据集的一阶段和两阶段探测器的性能。根据拟议的S2特性,我们分别对YOLO4-P5和YOLO4-P6进行了1.3%和1.1%的AP改进。我们分别对RANN和MASS R-N2的特性进行了观测。