While witnessed with rapid development, remote sensing object detection remains challenging for detecting high aspect ratio objects. This paper shows that large strip convolutions are good feature representation learners for remote sensing object detection and can detect objects of various aspect ratios well. Based on large strip convolutions, we build a new network architecture called Strip R-CNN, which is simple, efficient, and powerful. Unlike recent remote sensing object detectors that leverage large-kernel convolutions with square shapes, our Strip R-CNN takes advantage of sequential orthogonal large strip convolutions to capture spatial information. In addition, we enhance the localization capability of remote-sensing object detectors by decoupling the detection heads and equipping the localization head with strip convolutions to better localize the target objects. Extensive experiments on several benchmarks, e.g., DOTA, FAIR1M, HRSC2016, and DIOR, show that our Strip R-CNN can largely improve previous works. Notably, our 30M model achieves 82.75% mAP on DOTA-v1.0, setting a new state-of-the-art record.Code is available at https://github.com/YXB-NKU/Strip-R-CNN.

该研究针对遥感目标检测中高纵横比目标检测的难题，提出了一种创新的网络架构Strip R-CNN，利用大条带卷积提升特征表示能力。通过解耦检测头并使用条带卷积增强定位能力，实验表明该模型在多个基准测试中显著提高了检测精度，尤其在DOTA-v1.0上达到了82.75%的mAP，创造了新纪录。

条带R-CNN：用于遥感目标检测的大条带卷积