  Among several road hazards that are present in any paved way in the world,
potholes are one of the most annoying and also involving higher maintenance
costs. There exists an increasing interest on the automated detection of these
hazards enabled by technological and research progress. Our research work
tackled the challenge of pothole detection from images of real world road
scenes. The main novelty resides on the application of the latest progress in
AI to learn the visual appearance of potholes. We built a large dataset of
images with pothole annotations. They contained road scenes from different
cities in the world, taken with different cameras, vehicles and viewpoints
under varied environmental conditions. Then, we fine-tuned four different
object detection models based on Faster R-CNN and SSD deep neural networks. We
achieved high average precision and the pothole detector was tested on the
Nvidia DrivePX2 platform with GPGPU capability, which can be embedded on
vehicles. Moreover, it was deployed on a real vehicle to notify the detected
potholes to a given IoT platform as part of AUTOPILOT H2020 project.
