斯坦福 & Baidu (Andrew Ng)的车辆、车道检测论文：

贾伟中科院合肥

An Empirical Evaluation of Deep Learning on Highway Driving.pdf (3.2 MB, 下载次数: 3184)

2015-4-9 12:16 上传

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斯坦福 & Baidu (Andrew Ng)的论文：An Empirical Evaluation of Deep Learning on Highway Driving http://arxiv.org/abs/1504.01716 ， 用深度学习检测车道、车辆，实现自动驾驶，效果不错！50米以内，车道线检测三个指标（precision, recall, F)差不多100%；车辆检测性能指标也都大于90%


代码： github.com/brodyh/caffe





An Empirical Evaluation of Deep Learning on Highway DrivingBrody Huval, Tao Wang, Sameep Tandon, Jeff Kiske, Will Song, Joel Pazhayampallil, Mykhaylo Andriluka, Royce Cheng-Yue, Fernando Mujica, Adam Coates, Andrew Y. Ng
(Submitted on 7 Apr 2015)

Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to autonomous driving. To prepare deep learning for industry uptake and practical applications, neural networks will require large data sets that represent all possible driving environments and scenarios. We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection. We show how existing convolutional neural networks (CNNs) can be used to perform lane and vehicle detection while running at frame rates required for a real-time system. Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving.

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Subjects:	Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as:	arXiv:1504.01716 [cs.RO]
	(or arXiv:1504.01716v1 [cs.RO] for this version)

[size=12.960000038147px]Submission historyFrom: Brody Huval [view email]
[v1] Tue, 7 Apr 2015 19:41:59 GMT (3744kb,D)

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