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首页> 外文期刊>Procedia Computer Science >Real Time Detection of Speed Hump/Bump and Distance Estimation with Deep Learning using GPU and ZED Stereo Camera
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Real Time Detection of Speed Hump/Bump and Distance Estimation with Deep Learning using GPU and ZED Stereo Camera

机译:使用GPU和ZED立体相机实时检测速度驼峰/凹凸和距离估计

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摘要

Most of the humps in India are not being constructed and maintained according to the public safety guidelines of Indian Road Congress (IRC) i.e., IRC099, which is resulting in damage to the vehicles, severe discomfort to the driver and even causing loss of direction control which is leading to fatalities. Very few methods were discussed in literature for un-marked speed hump/bump detection.We propose a method that detects and informs the driver about the upcoming un-marked and marked speed hump/bump in real time using deep learning techniques and gives the distance the vehicle is away from it using stereo-vision approaches. We have achieved using NVIDIA GPU and Stereolabs ZED Stereo camera hardware. With this driver or autonomous mode of the vehicle can control the vehicle speeds to be at safer limits in order to not cause any kind of discomfort to the passengers as well as damage to the vehicle.
机译:印度的大多数驼峰没有根据印度道大会(IRC)的公共安全指南(IRC)IE,IRC099,这导致车辆损坏,对司机的严重不适,甚至导致方向控制丧失这导致死亡。对于未标记的速度驼峰/凹凸检测的文献中讨论了很少的方法。我们使用深度学习技术,提出了一种检测和通知驾驶员即将到来的未标记和标记速度驼峰的方法,并提供距离车辆远离它使用立体视觉方法。我们使用NVIDIA GPU和StereoLabs ZED立体声相机硬件实现了。利用这种驾驶员或自主模式的车辆可以控制车速处于更安全的限制,以便不会导致乘客的任何不适以及车辆损坏。

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