This review systematically examines the progression of the You Only Look Once
(YOLO) object detection algorithms from YOLOv1 to the recently unveiled
YOLOv10. Employing a reverse chronological analysis, this study examines the
advancements introduced by YOLO algorithms, beginning with YOLOv10 and
progressing through YOLOv9, YOLOv8, and subsequent versions to explore each
version's contributions to enhancing speed, accuracy, and computational
efficiency in real-time object detection. The study highlights the
transformative impact of YOLO across five critical application areas:
automotive safety, healthcare, industrial manufacturing, surveillance, and
agriculture. By detailing the incremental technological advancements that each
iteration brought, this review not only chronicles the evolution of YOLO but
also discusses the challenges and limitations observed in each earlier
versions. The evolution signifies a path towards integrating YOLO with
multimodal, context-aware, and General Artificial Intelligence (AGI) systems
for the next YOLO decade, promising significant implications for future
developments in AI-driven applications.

此综述系统地考察了 You Only Look Once（YOLO）目标检测算法从 YOLOv1 到最新发布的 YOLOv10 的发展进程。通过逆向时间顺序分析，研究探讨了 YOLO 算法带来的进步，从 YOLOv10 开始，逐步分析了 YOLOv9、YOLOv8 和后续版本在提高实时目标检测的速度、准确性和计算效率方面的贡献。该研究强调了 YOLO 在五个关键应用领域（汽车安全、医疗保健、工业制造、监视和农业）的革命性影响。通过详细描述每个版本的技术进步，本综述不仅记录了 YOLO 的演变，还讨论了早期版本中观察到的挑战和限制。这种演进标志着将 YOLO 与多模态、上下文感知和通用人工智能（AGI）系统相结合的路径，为下一个 YOLO 十年提供了重要的发展影响，对于 AI 驱动应用的未来发展具有重要意义。

YOLOv10 及其起源：一篇十年来全面回顾《You Only Look Once》系列的论文

YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You  Only Look Once Series

Introduction This study explores the use of the latest You Only Look Once
(YOLO V7) object detection method to enhance kidney detection in medical
imaging by training and testing a modified YOLO V7 on medical image formats.
Methods Study includes 878 patients with various subtypes of renal cell
carcinoma (RCC) and 206 patients with normal kidneys. A total of 5657 MRI scans
for 1084 patients were retrieved. 326 patients with 1034 tumors recruited from
a retrospective maintained database, and bounding boxes were drawn around their
tumors. A primary model was trained on 80% of annotated cases, with 20% saved
for testing (primary test set). The best primary model was then used to
identify tumors in the remaining 861 patients and bounding box coordinates were
generated on their scans using the model. Ten benchmark training sets were
created with generated coordinates on not-segmented patients. The final model
used to predict the kidney in the primary test set. We reported the positive
predictive value (PPV), sensitivity, and mean average precision (mAP). Results
The primary training set showed an average PPV of 0.94 +/- 0.01, sensitivity of
0.87 +/- 0.04, and mAP of 0.91 +/- 0.02. The best primary model yielded a PPV
of 0.97, sensitivity of 0.92, and mAP of 0.95. The final model demonstrated an
average PPV of 0.95 +/- 0.03, sensitivity of 0.98 +/- 0.004, and mAP of 0.95
+/- 0.01. Conclusion Using a semi-supervised approach with a medical image
library, we developed a high-performing model for kidney detection. Further
external validation is required to assess the model's generalizability.

通过使用最新的 You Only Look Once (YOLO V7) 目标检测方法，该研究针对医学图像格式上的肾脏检测进行了训练和测试，通过在 878 名患有各种亚型的肾细胞癌（RCC）和 206 名正常肾脏患者中检索了 1084 名患者的 5657 张 MRI 扫描，观察最终模型的肯定预测值（PPV）、敏感性和平均精确度 (mAP)。

使用 YOLO v7 在磁共振成像中检测肾脏：一种有监督对比学习

Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging: A  Supervised Contrastive Learning

The You Only Look Once (YOLO) series of detectors have established themselves
as efficient and practical tools. However, their reliance on predefined and
trained object categories limits their applicability in open scenarios.
Addressing this limitation, we introduce YOLO-World, an innovative approach
that enhances YOLO with open-vocabulary detection capabilities through
vision-language modeling and pre-training on large-scale datasets.
Specifically, we propose a new Re-parameterizable Vision-Language Path
Aggregation Network (RepVL-PAN) and region-text contrastive loss to facilitate
the interaction between visual and linguistic information. Our method excels in
detecting a wide range of objects in a zero-shot manner with high efficiency.
On the challenging LVIS dataset, YOLO-World achieves 35.4 AP with 52.0 FPS on
V100, which outperforms many state-of-the-art methods in terms of both accuracy
and speed. Furthermore, the fine-tuned YOLO-World achieves remarkable
performance on several downstream tasks, including object detection and
open-vocabulary instance segmentation.

YOLO-World is an innovative approach that enhances the You Only Look Once (YOLO) series of detectors with open-vocabulary detection capabilities through vision-language modeling, achieving high efficiency and accuracy in detecting a wide range of objects in a zero-shot manner.

YOLO-World：实时开放词汇物体检测

YOLO-World: Real-Time Open-Vocabulary Object Detection

You Only Look Once (YOLO) algorithm is a representative target detection
algorithm emerging in 2016, which is known for its balance of computing speed
and accuracy, and now plays an important role in various fields of human
production and life. However, there are still many limitations in the
application of YOLO algorithm in underwater environments due to problems such
as dim light and turbid water. With limited land area resources, the ocean must
have great potential for future human development. In this paper, starting from
the actual needs of marine engineering applications, taking underwater
structural health monitoring (SHM) and underwater biological detection as
examples, we propose improved methods for the application of underwater YOLO
algorithms, and point out the problems that still exist.

YOLO 算法在水下环境中的应用存在限制，本文从海洋工程应用的实际需求出发，以水下结构健康监测和水下生物检测为例，提出了改进的水下 YOLO 算法应用方法，并指出了仍然存在的问题。