PKINet-v2: Towards Powerful and Efficient Poly-Kernel Remote Sensing Object Detection

Object detection in remote sensing images (RSIs) is chal-lenged by the coexistence of geometric and spatial complexity: targetsmay appear with diverse aspect ratios, while spanning a wide range ofobject sizes under varied contexts. Existing RSI backbones address thetwo challenges separately, either by adopting anisotropic strip kernelsto model slender targets or by using isotropic large kernels to capturebroader context. However, such isolated treatments lead to complemen-tary drawbacks: the strip-only design can disrupt spatial coherence forregular-shaped objects and weaken tiny details, whereas isotropic largekernels often introduce severe background noise and geometric mismatchfor slender structures. In this paper, we extend PKINet, and presenta powerful and efficient backbone that jointly handles both challengeswithin a unified paradigm named Poly Kernel Inception Network v2(PKINet-v2). PKINet-v2 synergizes anisotropic axial-strip convolutionswith isotropic square kernels and builds a multi-scope receptive field, pre-serving fine-grained local textures while progressively aggregating long-range context across scales. To enable efficient deployment, we furtherintroduce a Heterogeneous Kernel Re-parameterization (HKR) Strategythat fuses all heterogeneous branches into a single depth-wise convolutionfor inference, eliminating fragmented kernel launches without accuracyloss. Extensive experiments on four widely-used benchmarks, includingDOTA-v1.0, DOTA-v1.5, HRSC2016, and DIOR-R, demonstrate thatPKINet-v2 achieves state-of-the-art accuracy while delivering a 3.9×FPS acceleration compared to PKINet-v1, surpassing previous remotesensing backbones in both effectiveness and efficiency.