Cross-Weather Road Scene Re-Identification Based on CNN
首发时间:2017-11-24
Abstract:In recent years, researches in the field of autonomous driving are in full swing. Among them, the road detection technology is the key. This technology uses sensors to analyze road forward derection, traffic signs, road lines, pedestrains\' status and other information in real-time. Because the road environment is very complex, the road detection algorithm must be robust against illumination changes, different weather conditions etc. In real life, the road environment changes little, and the daily driving routes are mostly in the same section. Based on these assumptions, this paper propased a way to tranform the road image which are under poor environmental conditions into images that are taken in the same place but are under good environmental conditions, and then to carry out road detection in subsequent mudules. In order to verify the idea, a road image database was established firstly, and the system was completed base on the classical CNN network. The expected results were obtained. In order to improve system\'s accuracy, this paper fine tuned the parameters of the neural network in the classical CNN. Afterwards, the transformation was learned on the basis of the CNN features and then the CNN features were projected into the domain-invariant feature space which was immune to drastic weather or illumination changes. Finally, the experiments have proved the practicality and the validity of the system.
keywords: deep learning scene retrieval road detection re-identification neural network
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一种基于CNN的跨天气道路场景再识别算法研究
摘要:近年来,自动驾驶领域的研究如火如荼。这其中,道路检测技术是关键,它通过传感器实时分析道路前进方向、道路交通警示牌、车道线、行人状态等信息。由于车辆行驶环境十分复杂,因此要求道路检测算法对于光照、天气等具有强抗干扰性。现实中,道路日常环境变化较小,而且日常的行驶路线较为单一。基于这些假设,本文提出将环境条件不好情况下的道路图像,通过图像检索、匹配等方式转为同一地点环境条件较好情况下图像,再传入后续模块进行道路检测。为验证设想,本文首先建立了道路图像数据库,并基于AlexNet网络完成了系统的设计和设想,取得了预期结果。然后通过参数微调、特征变换等方式,提高了系统的准确率。最后验证了系统的实用性。
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一种基于CNN的跨天气道路场景再识别算法研究
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