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tion process which currently relies heavily on manual
human expertise.
In future work we will train with larger dataset(s).
In particular, we will train with more samples of im-
ages containing potholes that are close and far from
the camera. We will also use images that includes pot-
holes as well other objects including shadows, man-
hole as well as other common objects in pavement
imagery. In this work we have focused on open-
source images, but there are specified images that
are collected through commercial road inspection ve-
hicles and provide more consistent images of pave-
ments which can help to build more robust model spe-
cially for the task of automatic road inspection. Re-
cently other sources such as drone shots and vehicle
windscreen cameras are being used to collect pave-
ment data. Such images often contain a multitude
of objects such as vehicles, trees, traffic signs and
or/people. Such data would require pre-processing to
extract these objects before training for the pothole
detection task. Training object detection models with
larger dataset(s) will require very high computational
power and need more training time. We will also ex-
periment with tuning the hyper parameters and train-
ing the model with other feature extraction networks.
ACKNOWLEDGEMENT
This work was funded by Science Foundation Ireland
through the SFI Centre for Research Training in Ma-
chine Learning (18/CRT/6183).
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