# YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception

arXiv: https://arxiv.org/abs/2506.17733 · HTML: https://mlsanity.com/paper/2506.17733/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 41 references that are in the map.
- First posted on arXiv 21 Jun 2025.

## Builds on (most cited first, 27 of 27)

- [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)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025)
- [Towards Large-Scale Small Object Detection: Survey and Benchmarks](https://mlsanity.com/paper/2207.14096/) (2022)
- [RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer](https://mlsanity.com/paper/2407.17140/) (2024)
- [You Only Learn One Representation: Unified Network for Multiple Tasks](https://mlsanity.com/paper/2105.04206/) (2021)
- [YOLOv6 v3.0: A Full-Scale Reloading](https://mlsanity.com/paper/2301.05586/) (2023)
- [Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism](https://mlsanity.com/paper/2309.11331/) (2023)
- [YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detection](https://mlsanity.com/paper/2308.05480/) (2023)
- [Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation](https://mlsanity.com/paper/2408.04804/) (2024)
- [Hypergraph Vision Transformers: Images are More than Nodes, More than Edges](https://mlsanity.com/paper/2504.08710/) (2025)
- [A Survey on Hypergraph Mining: Patterns, Tools, and Generators](https://mlsanity.com/paper/2401.08878/) (2024)
- [HGFormer: Topology-Aware Vision Transformer with HyperGraph Learning](https://mlsanity.com/paper/2504.02440/) (2025)

## Built on by (most cited first, 30 of 58)

- [YOLO26: Key Architectural Enhancements and Performance Benchmarking for Real-Time Object Detection](https://mlsanity.com/paper/2509.25164/) (2025)
- [Ultralytics YOLO Evolution: An Overview of YOLO27, YOLO26, YOLO11, YOLOv8, and YOLOv5 Object Detectors for Computer Vision and Pattern Recognition](https://mlsanity.com/paper/2510.09653/) (2025)
- [NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge Report](https://mlsanity.com/paper/2604.17070/) (2026)
- [YOLO26: An Analysis of NMS-Free End to End Framework for Real-Time Object Detection](https://mlsanity.com/paper/2601.12882/) (2026)
- [QualiTeacher: Quality-Conditioned Pseudo-Labeling for Real-World Image Restoration](https://mlsanity.com/paper/2603.08030/) (2026)
- [YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection](https://mlsanity.com/paper/2512.23273/) (2025)
- [YOLOE-26: Integrating YOLO26 with YOLOE for Real-Time Open-Vocabulary Instance Segmentation](https://mlsanity.com/paper/2602.00168/) (2026)
- [A Comparative Benchmark of Real-time Detectors for Blueberry Detection towards Precision Orchard Management](https://mlsanity.com/paper/2509.20580/) (2025)
- [YOLO26: A Comprehensive Architecture Overview and Key Improvements](https://mlsanity.com/paper/2602.14582/) (2026)
- [LeAD-M3D: Leveraging Asymmetric Distillation for Real-Time Monocular 3D Detection](https://mlsanity.com/paper/2512.05663/) (2025)
- [MuS-Polar3D: A Benchmark Dataset for Computational Polarimetric 3D Imaging under Multi-Scattering Conditions](https://mlsanity.com/paper/2512.21513/) (2025)
- [Practical Insights into Semi-Supervised Object Detection Approaches](https://mlsanity.com/paper/2601.13380/) (2026)
- [Cross-Paradigm Evaluation of Gaze-Based Semantic Object Identification for Intelligent Vehicles](https://mlsanity.com/paper/2602.01452/) (2026)
- [GroundCount: Grounding Vision-Language Models with Object Detection for Mitigating Counting Hallucinations](https://mlsanity.com/paper/2603.10978/) (2026)
- [POS-ISP: Pipeline Optimization at the Sequence Level for Task-aware ISP](https://mlsanity.com/paper/2604.06938/) (2026)
- [WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects](https://mlsanity.com/paper/2604.07759/) (2026)
- [Beyond Mamba: Enhancing State-space Models with Deformable Dilated Convolutions for Multi-scale Traffic Object Detection](https://mlsanity.com/paper/2604.08038/) (2026)
- [Resource-Constrained UAV-Based Weed Detection for Site-Specific Management on Edge Devices](https://mlsanity.com/paper/2604.23442/) (2026)
- [Towards Self-Explainable Document Visual Question Answering with Chain-of-Explanation Predictions](https://mlsanity.com/paper/2605.06058/) (2026)
- [RT-SDGOD: Real-Time Single-Domain Generalized Object Detection](https://mlsanity.com/paper/2606.09367/) (2026)
- [CP-WSP: A Declarative CP-SAT Framework for Configurable Multi-Constraint Workforce Scheduling](https://mlsanity.com/paper/2607.05177/) (2026)
- [SBP-YOLO:A Lightweight Real-Time Model for Detecting Speed Bumps and Potholes toward Intelligent Vehicle Suspension Systems](https://mlsanity.com/paper/2508.01339/) (2025)
- [TinyDef-DETR: A Transformer-Based Framework for Defect Detection in Transmission Lines from UAV Imagery](https://mlsanity.com/paper/2509.06035/) (2025)
- [FMC-DETR: Frequency-Decoupled Multi-Domain Coordination for Aerial-View Object Detection](https://mlsanity.com/paper/2509.23056/) (2025)
- [ZeBROD: Zero-Retraining Based Recognition and Object Detection Framework](https://mlsanity.com/paper/2512.04888/) (2025)
- [VajraV1 -- The most accurate Real Time Object Detector of the YOLO family](https://mlsanity.com/paper/2512.13834/) (2025)
- [BiCoR-Seg: Bidirectional Co-Refinement Framework for High-Resolution Remote Sensing Image Segmentation](https://mlsanity.com/paper/2512.20255/) (2025)
- [SME-YOLO: A Real-Time Detector for Tiny Defect Detection on PCB Surfaces](https://mlsanity.com/paper/2601.11402/) (2026)
- [M2I2HA: Multi-modal Object Detection Based on Intra- and Inter-Modal Hypergraph Attention](https://mlsanity.com/paper/2601.14776/) (2026)
- [PovNet+: A Deep Learning Architecture for Socially Assistive Robots to Learn and Assist with Multiple Activities of Daily Living](https://mlsanity.com/paper/2602.00131/) (2026)

## Abstract (arXiv)

The YOLO series models reign supreme in real-time object detection due to their superior accuracy and computational efficiency. However, both the convolutional architectures of YOLO11 and earlier versions and the area-based self-attention mechanism introduced in YOLOv12 are limited to local information aggregation and pairwise correlation modeling, lacking the capability to capture global multi-to-multi high-order correlations, which limits detection performance in complex scenarios. In this paper, we propose YOLOv13, an accurate and lightweight object detector. To address the above-mentioned challenges, we propose a Hypergraph-based Adaptive Correlation Enhancement (HyperACE) mechanism that adaptively exploits latent high-order correlations and overcomes the limitation of previous methods that are restricted to pairwise correlation modeling based on hypergraph computation, achieving efficient global cross-location and cross-scale feature fusion and enhancement. Subsequently, we propose a Full-Pipeline Aggregation-and-Distribution (FullPAD) paradigm based on HyperACE, which effectively achieves fine-grained information flow and representation synergy within the entire network by distributing correlation-enhanced features to the full pipeline. Finally, we propose to leverage depthwise separable convolutions to replace vanilla large-kernel convolutions, and design a series of blocks that significantly reduce parameters and computational complexity without sacrificing performance. We conduct extensive experiments on the widely used MS COCO benchmark, and the experimental results demonstrate that our method achieves state-of-the-art performance with fewer parameters and FLOPs. Specifically, our YOLOv13-N improves mAP by 3.0% over YOLO11-N and by 1.5% over YOLOv12-N. The code and models of our YOLOv13 model are available at: https://github.com/iMoonLab/yolov13.