# Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-Art

arXiv: https://arxiv.org/abs/2309.04902 · HTML: https://mlsanity.com/paper/2309.04902/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 80 of its 134 references that are in the map.
- First posted on arXiv 10 Sep 2023.

## Builds on (most cited first, 30 of 80)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [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)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [Exploring Plain Vision Transformer Backbones for Object Detection](https://mlsanity.com/paper/2203.16527/) (2022)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://mlsanity.com/paper/2201.12329/) (2022)
- [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://mlsanity.com/paper/1912.02424/) (2019)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [DN-DETR: Accelerate DETR Training by Introducing Query DeNoising](https://mlsanity.com/paper/2203.01305/) (2022)
- [Cascade R-CNN: High Quality Object Detection and Instance Segmentation](https://mlsanity.com/paper/1906.09756/) (2019)

## Built on by (most cited first, 15 of 15)

- [Small Object Few-shot Segmentation for Vision-based Industrial Inspection](https://mlsanity.com/paper/2407.21351/) (2024)
- [Faster Image2Video Generation: A Closer Look at CLIP Image Embedding's Impact on Spatio-Temporal Cross-Attentions](https://mlsanity.com/paper/2407.19205/) (2024)
- [A multi-modal dataset for insect biodiversity with imagery and DNA at the trap and individual level](https://mlsanity.com/paper/2507.06972/) (2025)
- [Efficient Feature Fusion for UAV Object Detection](https://mlsanity.com/paper/2501.17983/) (2025)
- [Inland Waterway Object Detection in Multi-environment: Dataset and Approach](https://mlsanity.com/paper/2504.04835/) (2025)
- [Toward Onboard AI-Enabled Solutions to Space Object Detection for Space Sustainability](https://mlsanity.com/paper/2505.01650/) (2025)
- [MaskAttn-UNet: A Mask Attention-Driven Framework for Universal Low-Resolution Image Segmentation](https://mlsanity.com/paper/2503.10686/) (2025)
- [Cross-DINO: Cross the Deep MLP and Transformer for Small Object Detection](https://mlsanity.com/paper/2505.21868/) (2025)
- [RareSpot: Spotting Small and Rare Wildlife in Aerial Imagery with Multi-Scale Consistency and Context-Aware Augmentation](https://mlsanity.com/paper/2506.19087/) (2025)
- [Bridging the Scale Gap: Balanced Tiny and General Object Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2512.01665/) (2025)
- [UHR-Micro: Diagnosing and Mitigating the Resolution Illusion in Earth Observation VLMs](https://mlsanity.com/paper/2605.12237/) (2026)
- [Intelligent Power Grid Design Review via Active Perception-Enabled Multimodal Large Language Models](https://mlsanity.com/paper/2601.14261/) (2025)
- [LAF-YOLOv10 with Partial Convolution Backbone, Attention-Guided Feature Pyramid, Auxiliary P2 Head, and Wise-IoU Loss for Small Object Detection in Drone Aerial Imagery](https://mlsanity.com/paper/2602.13378/) (2026)
- [Zoom In, Reason Out: Efficient Far-field Anomaly Detection in Expressway Surveillance Videos via Focused VLM Reasoning Guided by Bayesian Inference](https://mlsanity.com/paper/2604.23724/) (2026)
- [Small Object Detection in Drone Aerial Imagery with LAF-YOLOv10](https://mlsanity.com/paper/2609.14560/) (2026)

## Abstract (arXiv)

Transformers have rapidly gained popularity in computer vision, especially in the field of object recognition and detection. Upon examining the outcomes of state-of-the-art object detection methods, we noticed that transformers consistently outperformed well-established CNN-based detectors in almost every video or image dataset. While transformer-based approaches remain at the forefront of small object detection (SOD) techniques, this paper aims to explore the performance benefits offered by such extensive networks and identify potential reasons for their SOD superiority. Small objects have been identified as one of the most challenging object types in detection frameworks due to their low visibility. We aim to investigate potential strategies that could enhance transformers' performance in SOD. This survey presents a taxonomy of over 60 research studies on developed transformers for the task of SOD, spanning the years 2020 to 2023. These studies encompass a variety of detection applications, including small object detection in generic images, aerial images, medical images, active millimeter images, underwater images, and videos. We also compile and present a list of 12 large-scale datasets suitable for SOD that were overlooked in previous studies and compare the performance of the reviewed studies using popular metrics such as mean Average Precision (mAP), Frames Per Second (FPS), number of parameters, and more. Researchers can keep track of newer studies on our web page, which is available at urlhttps://github.com/arekavandi/Transformer-SOD.