Potholes have over the last few years become more common on the roads that I use, and recently I’ve realised that the Government are more likely to fix these roads if somehow they could remotely detect them.
What it does
RoadAccountability is a simple web platform that would allow road users take picture of potholes they come across, and these potholes are checked by a deep learning model in the back to assert that they are indeed potholes, thereafter logging the pothole pictures and their locations to a map that can be seen by all.
How we built it
- Trained a deep learning model from Road data on Azure Machine Learning.
- Deployed the model as a web service running in an Azure Container Instance.
- Built the web app locally with Django, from Django the model is called during image uploads.
- If an Image sent to the model is deemed to be a Pothole, the image's metadata is extracted, and the image is then stored with its geographical location and timestamp.
- Using folium python library, the potholes are populated as markers on the map.
Challenges we ran into
Web Development : Not having any previous web development experience, it was a challenge getting the platform up and running in the way envisioned.
Accomplishments that we're proud of
The system flow, the POC's working parts give a glimpse of how massively effective and scalable this could be, given its practicability.
What's next for Road-Accountability
- Tie the loose ends and have the platform working as intended, and deployed.
- Maintain and build together with the opensource community, the web development part of it is a whole lot and crucial to usability. Community only can deem this useful, effective and determine its success.
- Improve Model Accuracy, currently at 76%. However there is a concern with the distribution of images used to train the model vs images likely to be uploaded by users. The images used in training were from a dashcam, and users are more likely to take images from a closer point of view with cellphones.