# ROI-Gated SAHI: Content-Adaptive Slicing-Based Inference for Efficient Object Detection

arXiv: https://arxiv.org/abs/2608.23923 · HTML: https://mlsanity.com/paper/2608.23923/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 25 Aug 2026.

## Builds on (most cited first, 7 of 7)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection](https://mlsanity.com/paper/2202.06934/) (2022)
- [Spatial Pruned Sparse Convolution for Efficient 3D Object Detection](https://mlsanity.com/paper/2209.14201/) (2022)
- [Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications](https://mlsanity.com/paper/2503.20516/) (2025)
- [Computer Vision for Primate Behavior Analysis in the Wild](https://mlsanity.com/paper/2401.16424/) (2024)
- [A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends](https://mlsanity.com/paper/2209.06399/) (2022)
- [Robust Foreground-Background Separation for Severely-Degraded Videos Using Convolutional Sparse Representation Modeling](https://mlsanity.com/paper/2506.17838/) (2025)

## Abstract (arXiv)

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 τ = 0.4 educes the mean latency, achieving a slight gain of 1.02× over Full SAHI. On a three-image sparse-to-dense case study, ROI-gating achieves speedups ranging from 0.96× to 6.90× with a mean speedup of 3.41×. These results show that ROI-gating is most beneficial in sparse scenes and requires policy-based routing for robust average behavior.