mlsanityData to 7 Oct 2026

Object Detection in 20 Years: A Survey

arXiv 1905.05055 ↗First posted 13 May 2019cs.CVcited by 528

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Abstract (arXiv)

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Over the past two decades, we have seen a rapid technological evolution of object detection and its profound impact on the entire computer vision field. If we consider today's object detection technique as a revolution driven by deep learning, then back in the 1990s, we would see the ingenious thinking and long-term perspective design of early computer vision. This paper extensively reviews this fast-moving research field in the light of technical evolution, spanning over a quarter-century's time (from the 1990s to 2022). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed-up techniques, and the recent state-of-the-art detection methods.

Key facts. Cited by 528 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 May 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 52; 2022: 63; 2023: 101; 2024: 139; 2025: 110; 2026: 63.

528 papers.

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  1. 2022Inner Monologue: Embodied Reasoning through Planning with Language Modelscited by 1,057
  2. 2023Segment Everything Everywhere All at Oncecited by 546
  3. 2024WebVoyager: Building an End-to-End Web Agent with Large Multimodal Modelscited by 424
  4. 2024YOLOv11: An Overview of the Key Architectural Enhancementscited by 414
  5. 2021A Survey of Human-in-the-loop for Machine Learningcited by 214
  6. 2023A Simple Framework for Open-Vocabulary Segmentation and Detectioncited by 197
  7. 2021Dynamic Head: Unifying Object Detection Heads with Attentionscited by 185
  8. 2023Semantic-SAM: Segment and Recognize Anything at Any Granularitycited by 185
  9. 2023RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Modelcited by 180
  10. 2022Towards Large-Scale Small Object Detection: Survey and Benchmarkscited by 119
  11. 20213D Object Detection for Autonomous Driving: A Surveycited by 106
  12. 2021A Survey of Modern Deep Learning based Object Detection Modelscited by 97
38 more
  1. 2021Unsupervised Domain Adaptation of Object Detectors: A Surveycited by 93
  2. 2025YOLO26: Key Architectural Enhancements and Performance Benchmarking for Real-Time Object Detectioncited by 76
  3. 2025Ovis-U1 Technical Reportcited by 65
  4. 2024ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterizationcited by 60
  5. 2025YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perceptioncited by 57
  6. 2022Instance Relation Graph Guided Source-Free Domain Adaptive Object Detectioncited by 55
  7. 2024UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Modelscited by 47
  8. 2022BadDet: Backdoor Attacks on Object Detectioncited by 45
  9. 2021A comprehensive review of Binary Neural Networkcited by 44
  10. 2023OvarNet: Towards Open-vocabulary Object Attribute Recognitioncited by 43
  11. 2021On Model Calibration for Long-Tailed Object Detection and Instance Segmentationcited by 39
  12. 2023Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perceptioncited by 38
  13. 2023Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attackcited by 37
  14. 2023GraphAlign: Enhancing Accurate Feature Alignment by Graph matching for Multi-Modal 3D Object Detectioncited by 34
  15. 2024AeroGen: Enhancing Remote Sensing Object Detection with Diffusion-Driven Data Generationcited by 32
  16. 2022Green Learning: Introduction, Examples and Outlookcited by 30
  17. 2023A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospectscited by 30
  18. 2023Towards Knowledge-driven Autonomous Drivingcited by 29
  19. 2024AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Modelscited by 29
  20. 2023CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detectioncited by 28
  21. 2024VISA: Retrieval Augmented Generation with Visual Source Attributioncited by 28
  22. 2021I3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectorscited by 27
  23. 2021SimROD: A Simple Adaptation Method for Robust Object Detectioncited by 27
  24. 2022Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processescited by 26
  25. 2023Intelligent Virtual Assistants with LLM-based Process Automationcited by 26
  26. 2022PillarGrid: Deep Learning-based Cooperative Perception for 3D Object Detection from Onboard-Roadside LiDARcited by 25
  27. 2022Continual Object Detection: A review of definitions, strategies, and challengescited by 25
  28. 2022GLFF: Global and Local Feature Fusion for AI-synthesized Image Detectioncited by 25
  29. 2024MIKE: A New Benchmark for Fine-grained Multimodal Entity Knowledge Editingcited by 25
  30. 2024A Comprehensive Survey on Evidential Deep Learning and Its Applicationscited by 25
  31. 2022The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Countingcited by 24
  32. 2024Prompt-Driven Dynamic Object-Centric Learning for Single Domain Generalizationcited by 24
  33. 2022Object Detection Using Sim2Real Domain Randomization for Robotic Applicationscited by 23
  34. 2021MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Reviewcited by 22
  35. 2021Backpropagation with Biologically Plausible Spatio-Temporal Adjustment For Training Deep Spiking Neural Networkscited by 22
  36. 2023Five A+ Network: You Only Need 9K Parameters for Underwater Image Enhancementcited by 22
  37. 2023Object Detection with Transformers: A Reviewcited by 22
  38. 2022Visual Clues: Bridging Vision and Language Foundations for Image Paragraph Captioningcited by 21

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