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. © 2018 The Authors. Published by Elsevier B.V.
cited By 0; Conference of 8th International Conference on Advances in Computing and Communications, ICACC 2018 ; Conference Date: 13 September 2018 Through 15 September 2018; Conference Code:142252
V. S. K. P. Varma, Adarsh, S., Dr. K. I. Ramachandran, and Nair, B. B., “Real time detection of speed hump/bump and distance estimation with deep learning using GPU and ZED stereo camera”, in Procedia Computer Science, 2018, vol. 143, pp. 988-997.