图像分类的目标检测可以参考哪篇文献?
时间: 2023-02-14 08:19:22 浏览: 54
图像分类的目标检测的经典文献之一是 "You Only Look Once: Unified, Real-Time Object Detection" (YOLO),作者是 Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi. 这篇文章于2016年发表在CVPR会议上,提出了一种新的目标检测方法,具有高精度和实时性。
相关问题
超高分辨率图像目标检测的相关参考文献
1. Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: towards real-time object detection with region proposal networks. In Advances in neural information processing systems (pp. 91-99).
2. Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2117-2125).
3. Redmon, J., & Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767.
4. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. In European conference on computer vision (pp. 21-37).
5. Law, H., & Deng, J. (2018). Cornernet: Detecting objects as paired keypoints. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 734-750).
6. Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018). Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 6848-6856).
7. Zhou, X., Wang, D., & Krähenbühl, P. (2019). Objects as points. arXiv preprint arXiv:1904.07850.
8. Law, H., & Deng, J. (2018). CornerNet-Lite: Efficient Keypoint-Based Object Detection. arXiv preprint arXiv:1904.08900.
列出遥感图像目标检测相关文献
我可以回答这个问题。关于遥感图像目标检测的相关文献,可以参考以下几篇:
1. "Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art" by Chen et al. (2017)
2. "Object Detection in Optical Remote Sensing Images: A Survey and a New Benchmark" by Xia et al. (2018)
3. "A Review of Deep Learning Methods for Object Detection in Remote Sensing Images" by Zhang et al. (2019)
4. "Object Detection in High Resolution Remote Sensing Images Based on Deep Learning" by Li et al. (2020)
以上是一些比较有代表性的文献,供您参考。