# Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection

arXiv: https://arxiv.org/abs/2202.06934 · HTML: https://mlsanity.com/paper/2202.06934/

## Key facts

- Cited by 105 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 14 Feb 2022.

## Builds on (most cited first, 14 of 14)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [VarifocalNet: An IoU-aware Dense Object Detector](https://mlsanity.com/paper/2008.13367/) (2020)
- [TOOD: Task-aligned One-stage Object Detection](https://mlsanity.com/paper/2108.07755/) (2021)
- [xView: Objects in Context in Overhead Imagery](https://mlsanity.com/paper/1802.07856/) (2018)
- [Augmentation for small object detection](https://mlsanity.com/paper/1902.07296/) (2019)
- [Satellite Imagery Multiscale Rapid Detection with Windowed Networks](https://mlsanity.com/paper/1809.09978/) (2018)

## Built on by (most cited first, 30 of 105)

- [STAR: A First-Ever Dataset and A Large-Scale Benchmark for Scene Graph Generation in Large-Size Satellite Imagery](https://mlsanity.com/paper/2406.09410/) (2024)
- [Visible-Thermal Tiny Object Detection: A Benchmark Dataset and Baselines](https://mlsanity.com/paper/2406.14482/) (2024)
- [FunGraph: Functionality Aware 3D Scene Graphs for Language-Prompted Scene Interaction](https://mlsanity.com/paper/2503.07909/) (2025)
- [Reconstructing Groups of People with Hypergraph Relational Reasoning](https://mlsanity.com/paper/2308.15844/) (2023)
- [Learning to Holistically Detect Bridges from Large-Size VHR Remote Sensing Imagery](https://mlsanity.com/paper/2312.02481/) (2023)
- [The 8th AI City Challenge](https://mlsanity.com/paper/2404.09432/) (2024)
- [1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results](https://mlsanity.com/paper/2211.13508/) (2022)
- [BaboonLand Dataset: Tracking Primates in the Wild and Automating Behaviour Recognition from Drone Videos](https://mlsanity.com/paper/2405.17698/) (2024)
- [Wheat3DGS: In-field 3D Reconstruction, Instance Segmentation and Phenotyping of Wheat Heads with Gaussian Splatting](https://mlsanity.com/paper/2504.06978/) (2025)
- [YOLC: You Only Look Clusters for Tiny Object Detection in Aerial Images](https://mlsanity.com/paper/2404.06180/) (2024)
- [Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery](https://mlsanity.com/paper/2411.02136/) (2024)
- [Stable Diffusion For Aerial Object Detection](https://mlsanity.com/paper/2311.12345/) (2023)
- [MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results](https://mlsanity.com/paper/2307.09143/) (2023)
- [deepNIR: Datasets for generating synthetic NIR images and improved fruit detection system using deep learning techniques](https://mlsanity.com/paper/2203.09091/) (2022)
- [The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024](https://mlsanity.com/paper/2311.14762/) (2023)
- [Comparative Analysis of YOLOv9, YOLOv10 and RT-DETR for Real-Time Weed Detection](https://mlsanity.com/paper/2412.13490/) (2024)
- [Improving Object Detection Quality in Football Through Super-Resolution Techniques](https://mlsanity.com/paper/2402.00163/) (2024)
- [SOAR: Advancements in Small Body Object Detection for Aerial Imagery Using State Space Models and Programmable Gradients](https://mlsanity.com/paper/2405.01699/) (2024)
- [Enhancing Maritime Situational Awareness through End-to-End Onboard Raw Data Analysis](https://mlsanity.com/paper/2411.03403/) (2024)
- [WildLive: Near Real-time Visual Wildlife Tracking onboard UAVs](https://mlsanity.com/paper/2504.10165/) (2025)
- [WIT-UAS: A Wildland-fire Infrared Thermal Dataset to Detect Crew Assets From Aerial Views](https://mlsanity.com/paper/2312.09159/) (2023)
- [DSORT-MCU: Detecting Small Objects in Real-Time on Microcontroller Units](https://mlsanity.com/paper/2410.16769/) (2024)
- [LRDDv2: Enhanced Long-Range Drone Detection Dataset with Range Information and Comprehensive Real-World Challenges](https://mlsanity.com/paper/2508.03331/) (2025)
- [A multi-modal dataset for insect biodiversity with imagery and DNA at the trap and individual level](https://mlsanity.com/paper/2507.06972/) (2025)
- [Edge-case Synthesis for Fisheye Object Detection: A Data-centric Perspective](https://mlsanity.com/paper/2507.16254/) (2025)
- [Learning Where to Embed: Noise-Aware Positional Embedding for Query Retrieval in Small-Object Detection](https://mlsanity.com/paper/2604.15065/) (2026)
- [Evaluation of YOLO Models with Sliced Inference for Small Object Detection](https://mlsanity.com/paper/2203.04799/) (2022)
- [Deep-NFA: a Deep textita contrario Framework for Small Object Detection](https://mlsanity.com/paper/2303.01363/) (2023)
- [Uncertainty-Aware Gradient Stabilization for Small Object Detection](https://mlsanity.com/paper/2303.01803/) (2023)
- [Enhancing Lightweight Neural Networks for Small Object Detection in IoT Applications](https://mlsanity.com/paper/2311.07163/) (2023)

## Abstract (arXiv)

Detection of small objects and objects far away in the scene is a major challenge in surveillance applications. Such objects are represented by small number of pixels in the image and lack sufficient details, making them difficult to detect using conventional detectors. In this work, an open-source framework called Slicing Aided Hyper Inference (SAHI) is proposed that provides a generic slicing aided inference and fine-tuning pipeline for small object detection. The proposed technique is generic in the sense that it can be applied on top of any available object detector without any fine-tuning. Experimental evaluations, using object detection baselines on the Visdrone and xView aerial object detection datasets show that the proposed inference method can increase object detection AP by 6.8%, 5.1% and 5.3% for FCOS, VFNet and TOOD detectors, respectively. Moreover, the detection accuracy can be further increased with a slicing aided fine-tuning, resulting in a cumulative increase of 12.7%, 13.4% and 14.5% AP in the same order. Proposed technique has been integrated with Detectron2, MMDetection and YOLOv5 models and it is publicly available at https://github.com/obss/sahi.git .