Vokhidov Husan, Hong Hyung Gil, Kang Jin Kyu, Hoang Toan Minh, Park Kang Ryoung
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea.
Sensors (Basel). 2016 Dec 16;16(12):2160. doi: 10.3390/s16122160.
Automobile driver information as displayed on marked road signs indicates the state of the road, traffic conditions, proximity to schools, etc. These signs are important to insure the safety of the driver and pedestrians. They are also important input to the automated advanced driver assistance system (ADAS), installed in many automobiles. Over time, the arrow-road markings may be eroded or otherwise damaged by automobile contact, making it difficult for the driver to correctly identify the marking. Failure to properly identify an arrow-road marker creates a dangerous situation that may result in traffic accidents or pedestrian injury. Very little research exists that studies the problem of automated identification of damaged arrow-road marking painted on the road. In this study, we propose a method that uses a convolutional neural network (CNN) to recognize six types of arrow-road markings, possibly damaged, by visible light camera sensor. Experimental results with six databases of Road marking dataset, KITTI dataset, Málaga dataset 2009, Málaga urban dataset, Naver street view dataset, and Road/Lane detection evaluation 2013 dataset, show that our method outperforms conventional methods.
标在道路标志上的汽车驾驶员信息指示了道路状况、交通情况、学校位置等。这些标志对于确保驾驶员和行人的安全很重要。它们也是许多汽车中安装的自动高级驾驶辅助系统（ADAS）的重要输入信息。随着时间的推移，箭头道路标记可能会因汽车接触而磨损或受到其他损坏，这使得驾驶员难以正确识别该标记。未能正确识别箭头道路标记会造成危险情况，可能导致交通事故或行人受伤。关于道路上绘制的受损箭头道路标记的自动识别问题，现有研究非常少。在本研究中，我们提出了一种方法，该方法使用卷积神经网络（CNN）通过可见光相机传感器识别六种可能受损的箭头道路标记。使用道路标记数据集、KITTI数据集、2009年马拉加数据集、马拉加城市数据集、Naver街景数据集和2013年道路/车道检测评估数据集这六个数据库进行的实验结果表明，我们的方法优于传统方法。
