DRPN: Making CNN dynamically handle scale variation

Based on our observations of infrared targets, significant scale variation along sequence frames occurs with high frequency. This paper proposes a dynamic re-parameterization network (DRPN) to deal with the scale variation and balance the detection precision between small 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. The 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 efficient 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 the detector using DRPN as the basic structure rather than SKNet or TridentNet obtains the best performances.(c) 2022 Elsevier Inc. All rights reserved.