yolo参考文献

### 关于YOLO算法的学术参考资料 YOLO（You Only Look Once）是一种实时目标检测算法，在计算机视觉领域具有重要地位。以下是关于YOLO算法的一些经典学术参考资料： #### 原始论文 - **YOLOv1**: Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi首次提出了YOLO的概念，并将其作为单阶段的目标检测方法发布在2016年的CVPR会议上[^2]。 ```bibtex @inproceedings{redmon2016you, title={You only look once: Unified, real-time object detection}, author={Redmon, Joseph and Divvala, Santosh and Girshick, Ross and Farhadi, Ali}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, pages={779--788}, year={2016} } ``` - **YOLOv2 (YOLO9000)**: 这一版本改进了模型精度并引入了联合训练的方法，能够识别超过9000种对象类别[^3]。 ```bibtex @article{redmon2017yolo9000, title={Yolo9000: better, faster, stronger}, author={Redmon, Joseph and Farhadi, Ali}, journal={arXiv preprint arXiv:1612.08242}, year={2017} } ``` - **YOLOv3**: 提出了更深层次网络结构的支持以及多尺度预测功能，进一步提升了性能表现[^4]。 ```bibtex @article{redmon2018yolov3, title={Yolov3: An incremental improvement}, author={Redmon, Joseph and Farhadi, Ali}, journal={arXiv preprint arXiv:1804.02767}, year={2018} } ``` #### 后续发展与变体研究 随着技术进步，后续出现了多个基于YOLO框架的新版本及其优化方案： - **YOLOv4**: Emphasizes high performance without requiring specialized hardware such as Tensor Cores found within NVIDIA GPUs.[^5] ```bibtex @misc{bochkovskiy2020yolov4, title={YOLOv4: Optimal Speed and Accuracy of Object Detection}, author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao}, year={2020}, eprint={2004.10934}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` - **YOLOv5**: Developed by Ultralytics but not officially published via traditional academic channels; however, it remains widely used due to its efficiency improvements over previous versions. - **Scaled-YOLOv4 & YOLOR**: These represent further advancements focusing on scaling up architectures while maintaining speed/accuracy trade-offs.[^6] #### 数据集和实验资源链接 对于希望深入理解或复现这些工作的研究人员来说，访问官方GitHub仓库是非常有帮助的： - [Darknet Repository](https://github.com/pjreddie/darknet): Original implementation provided by Joseph Redmond. - [Ultralytics/YOLOv5](https://github.com/ultralytics/yolov5): A PyTorch-based version offering enhanced usability features compared to earlier implementations. 通过以上提到的各种版本及相关扩展工作可以全面了解YOLO系列的发展历程和技术细节变化情况。