  Modern object detection networks pursuit higher precision on general object
detection datasets, at the same time the computation burden is also increasing
along with the improvement of precision. Nevertheless, the inference time and
precision are both critical to object detection system which needs to be
real-time. It is necessary to research precision improvement without extra
computation cost. In this work, two modules are proposed to improve detection
precision with zero cost, which are focus on FPN and detection head improvement
for general object detection networks. We employ the scale attention mechanism
to efficiently fuse multi-level feature maps with less parameters, which is
called SA-FPN module. Considering the correlation of classification head and
regression head, we use sequential head to take the place of widely-used
parallel head, which is called Seq-HEAD module. To evaluate the effectiveness,
we apply the two modules to some modern state-of-art object detection networks,
including anchor-based and anchor-free. Experiment results on coco dataset show
that the networks with the two modules can surpass original networks by 1.1 AP
and 0.8 AP with zero cost for anchor-based and anchor-free networks,
respectively. Code will be available at https://git.io/JTFGl.
