Object detection in remote sensing images (RSIs) often suffers from several
increasing challenges, including the large variation in object scales and the
diverse-ranging context. Prior methods tried to address these challenges by
expanding the spatial receptive field of the backbone, either through
large-kernel convolution or dilated convolution. However, the former typically
introduces considerable background noise, while the latter risks generating
overly sparse feature representations. In this paper, we introduce the Poly
Kernel Inception Network (PKINet) to handle the above challenges. PKINet
employs multi-scale convolution kernels without dilation to extract object
features of varying scales and capture local context. In addition, a Context
Anchor Attention (CAA) module is introduced in parallel to capture long-range
contextual information. These two components work jointly to advance the
performance of PKINet on four challenging remote sensing detection benchmarks,
namely DOTA-v1.0, DOTA-v1.5, HRSC2016, and DIOR-R.

本文介绍了一种用于遥感图像中的目标检测的多尺度多核卷积网络（PKINet），它使用多尺度卷积核提取不同尺度的目标特征并捕捉局部上下文信息，并引入了上下文锚点注意力模块来捕捉远程上下文信息，这两个组件共同推进了 PKINet 在四个具有挑战性的遥感检测基准上的性能提升。