通过兴趣区域筛选，让目标检测只在关键区域计算，提升效率。
ROI-Gated SAHI: Content-Adaptive Slicing-Based Inference for Efficient Object Detection
- 用轻量级提案器定位前景区域，仅对重要区域进行切片推理。
- 在稀疏场景中平均提速3.41倍，最高峰值达6.90倍。
- 适合高分辨率图像中目标稀疏的场景，尤其利于资源受限部署。
切片辅助超推理（SAHI）虽能提升高分辨率图像中的小目标检测效果，但常在背景区域耗费大量计算资源。本文提出基于感兴趣区域（ROI）门控的SAHI，引入轻量级提案器定位前景区域，仅在有信息量的区域执行切片优化。在包含128张图像的COCO128全集上，静态ROI门控平均速度比完整SAHI慢，速度比为0.88，且[email protected]降至0.6602（完整SAHI为0.7569）。采用τ=0.4的简单自适应路由策略后，平均延迟降低，速度提升1.02×。在三张图像的稀疏到密集案例研究中，速度提升范围为0.96×至6.90×，平均提升3.41×。结果表明，ROI门控在稀疏场景中优势显著，需依赖策略路由以保证稳定性能。
原文摘要 · Abstract (English)
Slicing-Aided Hyper Inference (SAHI) improves small object detection in high-resolution images but often spends substantial compute on background tiles. We propose region-of-interest (ROI)-Gated SAHI, an inference-time framework that introduces a lightweight proposer to localize foreground regions and restrict sliced refinement to informative areas. We evaluate the framework in two settings. On the COCO128 full split dataset comprising 128 images, static ROI-gating is slower on average than Full SAHI, achieving a speed ratio of 0.88, and yields a lower [email protected] of 0.6602 compared with 0.7569 for Full SAHI. A simple adaptive routing policy with $τ=$ 0.4 educes the mean latency, achieving a slight gain of 1.02$\times$ over Full SAHI. On a three-image sparse-to-dense case study, ROI-gating achieves speedups ranging from 0.96$\times$ to 6.90$\times$ with a mean speedup of 3.41$\times$. These results show that ROI-gating is most beneficial in sparse scenes and requires policy-based routing for robust average behavior.
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