  FPN-based detectors have made significant progress in general object
detection, e.g., MS COCO and PASCAL VOC. However, these detectors fail in
certain application scenarios, e.g., tiny object detection. In this paper, we
argue that the top-down connections between adjacent layers in FPN bring
two-side influences for tiny object detection, not only positive. We propose a
novel concept, fusion factor, to control information that deep layers deliver
to shallow layers, for adapting FPN to tiny object detection. After series of
experiments and analysis, we explore how to estimate an effective value of
fusion factor for a particular dataset by a statistical method. The estimation
is dependent on the number of objects distributed in each layer. Comprehensive
experiments are conducted on tiny object detection datasets, e.g., TinyPerson
and Tiny CityPersons. Our results show that when configuring FPN with a proper
fusion factor, the network is able to achieve significant performance gains
over the baseline on tiny object detection datasets. Codes and models will be
released.
