Jordan Bennett (Website).
Thanks Google, TensorRt creators, thanks jhasuman, for his desktop-version yolo-v2 based pothole detector.
There were lots of head scratching moments, but the tensorRT/jetson nano-mini computer version works fine, with seemingly similar accuracy to full Desktop version, as seen in Part B/4 Prediction.
Why do this? There is a lot to say about how damaging surprise potholes can be while driving, but instead I will leave this nice quick to the point summary here: "Youtube/Hitting a pothole in a Tesla costs 2600 US dollars". This doesn't only happen to teslas either!
That said, this Google Colab code is separate from the final product code I prepared for the jetson nano, although the nano code uses some of this colab code.
The jetson nano is a portable device, and hence this may be attached to a vehicle to do pothole detection, based on convolutional neural networks.
NVIDIA is a multi-billion dollar artificial intelligence involved company. Their technology has been central to a large degree of humanity's progress so far.
Either go to this quick NVIDIA Jestson link to my project, or go to NVIDIA's jetson project page, and scroll down until you see "Smart Pothole Detector" by Jordan. There you will also see many exciting/intriguing artificial intelligence/machine learning aligned projects.
Below is a screenshot of my pothole project on Nvidia's jetson project page:
Please follow all instructions outlined in this separate Readme.md file, found in the "jetson-nano-source-code" folder of this repository.
The first 4 steps below were added by Jordan, and other steps added/modified to align with custom pothole model, based on this original blog/colab code.
Find out how costly it would be to incoroprate a dark to bright neural network converter, to enable pothole detection at night.
Live speedbump detection. (Especially those haphazzardly painted ones that are painted black like road)
Other obstacle detection, that may be to thin for car sensors to pick up.