  Based on our observations of infrared targets, serious scale variation along
within sequence frames has high-frequently occurred. In this paper, we propose
a dynamic re-parameterization network (DRPN) to deal with the scale variation
and balance the detection precision between small targets and large targets in
infrared datasets. DRPN adopts the multiple branches with different sizes of
convolution kernels and the dynamic convolution strategy. Multiple branches
with different sizes of convolution kernels have different sizes of receptive
fields. Dynamic convolution strategy makes DRPN adaptively weight multiple
branches. DRPN can dynamically adjust the receptive field according to the
scale variation of the target. Besides, in order to maintain effective
inference in the test phase, the multi-branch structure is further converted to
a single-branch structure via the re-parameterization technique after training.
Extensive experiments on FLIR, KAIST, and InfraPlane datasets demonstrate the
effectiveness of our proposed DRPN. The experimental results show that
detectors using the proposed DRPN as the basic structure rather than SKNet or
TridentNet obtained the best performances.
