用长条形卷积提升遥感目标检测，尤其擅长识别细长物体。
Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection
- 用纵向和横向的长条形卷积替代方形大核卷积，更好捕捉遥感图像空间特征。
- 在DOTA数据集上达到82.75% mAP，3000万参数模型刷新遥感检测新纪录。
- 结构简洁高效，适合处理高宽比差异大的遥感目标，如飞机、船舶等。
尽管发展迅速，遥感目标检测仍面临识别高宽比物体的挑战。本文表明，大尺寸条带卷积能有效学习遥感图像特征，可良好检测多种宽高比的目标。基于此，我们提出新的网络架构Strip R-CNN，结构简单、高效且强大。与近期采用方形大核卷积的遥感检测器不同，我们的Strip R-CNN在骨干网络StripNet中使用顺序正交的长条形卷积，以捕获空间信息。同时，通过解耦检测头并为定位分支引入条带卷积，显著提升定位精度。在多个基准测试上，包括DOTA、FAIR1M、HRSC2016和DIOR，实验结果表明，Strip R-CNN显著优于现有方法。特别地，3000万参数模型在DOTA-v1.0上达到82.75% mAP，创下新纪录。代码将公开于https://github.com/YXB-NKU/Strip-R-CNN。
原文摘要 · Abstract (English)
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 in our backbone network StripNet to capture spatial information. In addition, we improve the localization capability of remote-sensing object detectors by decoupling the detection heads and equipping the localization branch with strip convolutions in our strip head. Extensive experiments on several benchmarks, for example DOTA, FAIR1M, HRSC2016, and DIOR, show that our Strip R-CNN can greatly improve previous work. In particular, our 30M model achieves 82.75% mAP on DOTA-v1.0, setting a new state-of-the-art record. Our code will be made publicly available.Code is available at https://github.com/YXB-NKU/Strip-R-CNN.
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