mlsanityData to 14 Oct 2026

YOLOv11: An Overview of the Key Architectural Enhancements

arXiv 2410.17725 ↗First posted 23 Oct 2024cs.CVcited by 425

Not on the map. Why?

Cited by 30 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

This study presents an architectural analysis of YOLOv11, the latest iteration in the YOLO (You Only Look Once) series of object detection models. We examine the models architectural innovations, including the introduction of the C3k2 (Cross Stage Partial with kernel size 2) block, SPPF (Spatial Pyramid Pooling - Fast), and C2PSA (Convolutional block with Parallel Spatial Attention) components, which contribute in improving the models performance in several ways such as enhanced feature extraction. The paper explores YOLOv11's expanded capabilities across various computer vision tasks, including object detection, instance segmentation, pose estimation, and oriented object detection (OBB). We review the model's performance improvements in terms of mean Average Precision (mAP) and computational efficiency compared to its predecessors, with a focus on the trade-off between parameter count and accuracy. Additionally, the study discusses YOLOv11's versatility across different model sizes, from nano to extra-large, catering to diverse application needs from edge devices to high-performance computing environments. Our research provides insights into YOLOv11's position within the broader landscape of object detection and its potential impact on real-time computer vision applications.

Key facts. Cited by 425 papers in the map (data through 14 Oct 2026). Builds on 11 of its 22 references that are in the map. First posted on arXiv 23 Oct 2024.

Builds on

11 of 22 references.

References · 11

  1. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  2. 2018YOLOv3: An Incremental Improvementcited by 2,659
  3. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
  4. 2016YOLO9000: Better, Faster, Strongercited by 1,431
  5. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
  6. 2019Object Detection in 20 Years: A Surveycited by 531
  7. 2018Object Detection with Deep Learning: A Reviewcited by 528
  8. 2024YOLOv10: Real-Time End-to-End Object Detectioncited by 515
  9. 2024YOLOv9: Learning What You Want to Learn Using Programmable Gradient Informationcited by 425
  10. 2022YOLOv6: A Single-Stage Object Detection Framework for Industrial Applicationscited by 275
  11. 2024What is YOLOv5: A deep look into the internal features of the popular object detectorcited by 37

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 16; 2025: 220; 2026: 189.

425 papers.

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50 most cited of 425 All 425 (JSON)