mlsanityData to 14 Oct 2026

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges

arXiv 2508.02067 ↗First posted 4 Aug 2025cs.CVcited by 5

Not on the map. Why?

Abstract (arXiv)

Over the past decade, object detection has advanced significantly, with the YOLO (You Only Look Once) family of models transforming the landscape of real-time vision applications through unified, end-to-end detection frameworks. From YOLOv1's pioneering regression-based detection to the latest YOLOv9, each version has systematically enhanced the balance between speed, accuracy, and deployment efficiency through continuous architectural and algorithmic advancements.. Beyond core object detection, modern YOLO architectures have expanded to support tasks such as instance segmentation, pose estimation, object tracking, and domain-specific applications including medical imaging and industrial automation. This paper offers a comprehensive review of the YOLO family, highlighting architectural innovations, performance benchmarks, extended capabilities, and real-world use cases. We critically analyze the evolution of YOLO models and discuss emerging research directions that extend their impact across diverse computer vision domains.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 25 of its 36 references that are in the map. First posted on arXiv 4 Aug 2025.

Builds on

25 of 36 references.

References · 25

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  3. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  4. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  5. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  6. 2014Going Deeper with Convolutionscited by 4,241
  7. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  8. 2015Fast R-CNNcited by 2,933
  9. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,884
  10. 2018YOLOv3: An Incremental Improvementcited by 2,659
  11. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
  12. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
13 more
  1. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,577
  2. 2016YOLO9000: Better, Faster, Strongercited by 1,431
  3. 2019Objects as Pointscited by 1,175
  4. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,020
  5. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
  6. 2018Path Aggregation Network for Instance Segmentationcited by 798
  7. 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 720
  8. 2021RepVGG: Making VGG-style ConvNets Great Againcited by 522
  9. 2024YOLOv9: Learning What You Want to Learn Using Programmable Gradient Informationcited by 425
  10. 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
  11. 2022YOLOv6: A Single-Stage Object Detection Framework for Industrial Applicationscited by 275
  12. 2018DropBlock: A regularization method for convolutional networkscited by 262
  13. 2021OTA: Optimal Transport Assignment for Object Detectioncited by 178

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025InfraGPT Smart Infrastructure: An End-to-End VLM-Based Framework for Detecting and Managing Urban Defectscited by 3
  2. 2025LeAD-M3D: Leveraging Asymmetric Distillation for Real-Time Monocular 3D Detectioncited by 1
  3. 2025Computer vision training dataset generation for robotic environments using Gaussian splattingcited by 0
  4. 2025CLIP-Joint-Detect: End-to-End Joint Training of Object Detectors with Contrastive Vision-Language Supervisioncited by 0
  5. 2026One-Stage Object Detectors in Autonomous Drivingcited by 0