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ROI-Gated SAHI: Content-Adaptive Slicing-Based Inference for Efficient Object Detection
arXiv:2608.23923v1 Announce Type: new Abstract: Slicing-Aided Hyper Inference (SAHI) improves small object detection in high-resolution images but often spends substantial compute on background tiles.
arXiv:2608.23923v1 Announce Type: new Abstract: 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 mAP@0.5 of 0.6602 compared with 0.7569 for Full SAHI. A simple adaptive routing policy with au = 0.4 educes the mean latency, achieving a slight gain of 1.02imes over Full SAHI. On a three-image sparse-to-dense case study, ROI-gating achieves speedups ranging from 0.96imes to 6.90imes with a mean speedup of 3.41imes. 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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Source: arXiv cs.CV | 2026-08-26