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YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series

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arxiv 2406.19407 v8 pith:7MXBUKHZ submitted 2024-06-12 cs.CV

Ranjan Sapkota , Marco Flores Calero , Rizwan Qureshi , Chetan Badgujar , Upesh Nepal , Alwin Poulose , Peter Zeno , Uday Bhanu Prakash Vaddevolu
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Sheheryar Khan Maged Shoman Hong Yan Manoj Karkee
This is my paper · ORCID
classification cs.CV
keywords yoloreviewdetectionversionsadvancementsobjectadvancesalgorithms
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This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv12. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv12 and progressing through YOLO11 (or YOLOv11), YOLOv10, YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, detection accuracy, and computational efficiency in real-time object detection. Additionally, this study reviews the alternative versions derived from YOLO architectural advancements of YOLO-NAS, YOLO-X, YOLO-R, DAMO-YOLO, and Gold-YOLO. Moreover, the study highlights the transformative impact of YOLO models across five critical application areas: autonomous vehicles and traffic safety, healthcare and medical imaging, industrial manufacturing, surveillance and security, and agriculture. By detailing the incremental technological advancements in subsequent YOLO versions, this review chronicles the evolution of YOLO, and discusses the challenges and limitations in each of the earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and Artificial General Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications. YOLO Review, YOLO Advances, YOLOv13, YOLOv14, YOLOv15, YOLOv16, YOLOv17, YOLOv18, YOLOv19, YOLOv20, YOLO review, YOLO Object Detection

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Forward citations

Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Efficient Vision-based Vehicle Speed Estimation

    cs.CV 2025-05 conditional novelty 5.0 of 10

    Replacing the detector in a vanishing-point-based speed estimation pipeline with YOLOv6 plus post-training quantization yields comparable or better speed accuracy at substantially higher frame rates on BrnoCompSpeed.

  2. Real-Time Brain Tumor Detection in Intraoperative Ultrasound Using YOLO11: From Model Training to Deployment in the Operating Room

    eess.IV 2025-01 conditional novelty 5.0 of 10

    A YOLO11-based detector identifies brain tumors in intraoperative ultrasound in real time and was qualitatively validated in 15 consecutive surgeries.

  3. Smart Parking with Pixel-Wise ROI Selection for Vehicle Detection Using YOLOv8, YOLOv9, YOLOv10, and YOLOv11

    cs.CV 2024-12 reject novelty 5.0 of 10

    A post-processing pixel-wise ROI mask applied to pretrained YOLO detections improved parking occupancy counting, with YOLOv9e reaching 99.68% balanced accuracy on a custom dataset, though the evaluation metrics are no...

  4. A Multi-Task Deep Learning Framework for Real-Time Intelligent Video Surveillance with Temporal Event Validation

    cs.CV 2026-07 conditional novelty 4.0 of 10

    Temporal multi-frame validation around concurrent CV detectors cuts fire false alarms from 52% to 4% and lifts video plate exact-match from 66.7% to 81.8% at real-time latency on one commodity GPU.

  5. HOTSPOT-YOLO: A Lightweight Deep Learning Attention-Driven Model for Detecting Thermal Anomalies in Drone-Based Solar Photovoltaic Inspections

    eess.IV 2025-08 reject novelty 4.0 of 10

    A YOLOv11 variant with EfficientNet and squeeze-and-excitation attention is claimed to find PV thermal anomalies at 90.8% mAP, but no code, data, or baseline training details are provided.

  6. RF-DETR Object Detection vs YOLOv12 : A Study of Transformer-based and CNN-based Architectures for Single-Class and Multi-Class Greenfruit Detection in Complex Orchard Environments Under Label Ambiguity

    cs.CV 2025-04 conditional novelty 4.0 of 10

    On an 857-image orchard dataset, RF-DETR achieved the best mAP@50 for both single-class (0.9464) and multi-class (0.8298) greenfruit detection, while YOLOv12N and YOLOv12L led mAP@50:95 in the two settings.

  7. Integrating YOLO11 and Convolution Block Attention Module for Multi-Season Segmentation of Tree Trunks and Branches in Commercial Apple Orchards

    cs.CV 2024-12 reject novelty 4.0 of 10

    A YOLO11-CBAM model trained on mixed dormant and canopy season images segments apple tree trunks and branches, but year-round generalization is only qualitatively demonstrated.

  8. Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data

    cs.CV 2024-11 conditional novelty 4.0 of 10

    A two-stage YOLO + Phi-3.5 pipeline that reads bounding-box labels to identify species and uses RAG to answer ecological questions achieves high F1 on camera-trap images, but no code or data are released.

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