[HTML][HTML] A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas

J Terven, DM Córdova-Esparza… - Machine Learning and …, 2023 - mdpi.com
YOLO has become a central real-time object detection system for robotics, driverless cars,
and video monitoring applications. We present a comprehensive analysis of YOLO's …

Object detection using deep learning, CNNs and vision transformers: A review

AB Amjoud, M Amrouch - IEEE Access, 2023 - ieeexplore.ieee.org
Detecting objects remains one of computer vision and image understanding applications'
most fundamental and challenging aspects. Significant advances in object detection have …

Yolov9: Learning what you want to learn using programmable gradient information

CY Wang, IH Yeh, HY Mark Liao - European conference on computer …, 2024 - Springer
Today's deep learning methods focus on how to design the objective functions to make the
prediction as close as possible to the target. Meanwhile, an appropriate neural network …

Diffusiondet: Diffusion model for object detection

S Chen, P Sun, Y Song, P Luo - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
We propose DiffusionDet, a new framework that formulates object detection as a denoising
diffusion process from noisy boxes to object boxes. During the training stage, object boxes …

YOLOv6: A single-stage object detection framework for industrial applications

C Li, L Li, H Jiang, K Weng, Y Geng, L Li, Z Ke… - arXiv preprint arXiv …, 2022 - arxiv.org
For years, the YOLO series has been the de facto industry-level standard for efficient object
detection. The YOLO community has prospered overwhelmingly to enrich its use in a …

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

CY Wang, A Bochkovskiy… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Real-time object detection is one of the most important research topics in computer vision.
As new approaches regarding architecture optimization and training optimization are …

Rtmdet: An empirical study of designing real-time object detectors

C Lyu, W Zhang, H Huang, Y Zhou, Y Wang… - arXiv preprint arXiv …, 2022 - arxiv.org
In this paper, we aim to design an efficient real-time object detector that exceeds the YOLO
series and is easily extensible for many object recognition tasks such as instance …

Bytetrack: Multi-object tracking by associating every detection box

Y Zhang, P Sun, Y Jiang, D Yu, F Weng, Z Yuan… - European conference on …, 2022 - Springer
Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in
videos. Most methods obtain identities by associating detection boxes whose scores are …

Tood: Task-aligned one-stage object detection

C Feng, Y Zhong, Y Gao, MR Scott… - 2021 IEEE/CVF …, 2021 - computer.org
One-stage object detection is commonly implemented by optimizing two sub-tasks: object
classification and localization, using heads with two parallel branches, which might lead to a …

Dense distinct query for end-to-end object detection

S Zhang, X Wang, J Wang, J Pang… - Proceedings of the …, 2023 - openaccess.thecvf.com
One-to-one label assignment in object detection has successfully obviated the need of non-
maximum suppression (NMS) as a postprocessing and makes the pipeline end-to-end …