车辆识别参考文献：15篇深度学习与目标检测必读论文

车辆识别参考文献：15篇深度学习与目标检测必读论文对车辆识别的研究有兴趣吗？这里精选了 15 篇涵盖车辆识别不同方面的参考文献，包括基于深度学习的方法、特征提取和目标检测等，希望能为您的研究提供帮助。1. Sermanet, P., Kavukcuoglu, K., Chintala, S., & LeCun, Y. (2012). Convolutional neural networks applied to house numbers digit classification. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR) (pp. 3288-3291). /这篇论文探讨了卷积神经网络在数字识别中的应用，对于理解卷积神经网络在车辆识别中的应用很有帮助。2. Farhadi, A., Endres, I., Hoiem, D., & Forsyth, D. (2009). Describing objects by their attributes. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1778-1785)./这篇论文介绍了通过属性描述物体的方法，可以应用于车辆识别中的特征提取。3. Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (Vol. 1, pp. 886-893)./方向梯度直方图(HOG)是一种经典的特征描述子，这篇论文详细介绍了HOG特征的提取方法，可用于车辆识别。4. Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 40(4), 834-848./DeepLab是一种基于深度学习的图像语义分割模型，可用于车辆识别中的像素级识别。5. Geiger, A., Lenz, P., & Urtasun, R. (2012). Are we ready for autonomous driving? The KITTI vision benchmark suite. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3354-3361)./KITTI数据集是自动驾驶领域常用的数据集，这篇论文介绍了KITTI数据集，并对自动驾驶技术的现状进行了分析。6. Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527-1554./深度置信网络(DBN)是一种经典的深度学习模型，这篇论文介绍了DBN的快速学习算法。7. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556./VGG网络是一种经典的深度卷积神经网络，这篇论文介绍了VGG网络的结构和训练方法。8. 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 (NIPS) (pp. 91-99)./Faster R-CNN是一种快速的目标检测模型，这篇论文介绍了Faster R-CNN的模型结构和训练方法。9. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 779-788)./YOLO是一种快速的目标检测模型，这篇论文介绍了YOLO的模型结构和训练方法。10. 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 (ECCV) (pp. 21-37)./SSD是一种快速的目标检测模型，这篇论文介绍了SSD的模型结构和训练方法。11. 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 (CVPR) (pp. 2117-2125)./特征金字塔网络(FPN)是一种用于目标检测的特征提取方法，这篇论文介绍了FPN的模型结构和训练方法。12. Li, Y., Qi, H., Dai, J., Ji, X., & Wei, Y. (2017). Fully convolutional instance-aware semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2359-2367)./这篇论文介绍了一种全卷积实例感知语义分割方法，可以用于车辆识别中的实例分割。13. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (NIPS) (pp. 1097-1105)./AlexNet是一种经典的深度卷积神经网络，这篇论文介绍了AlexNet的模型结构和训练方法。14. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770-778)./残差网络(ResNet)是一种经典的深度学习模型，这篇论文介绍了ResNet的模型结构和训练方法。15. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 580-587)./R-CNN是一种经典的目标检测模型，这篇论文介绍了R-CNN的模型结构和训练方法。希望这些参考文献能够为您的车辆识别研究提供帮助！