  The road is vital for many aspects of life, and road maintenance is crucial
for human safety. One of the critical tasks to allow timely repair of road
damages is to quickly and efficiently detect and classify them. This work
details the strategies and experiments evaluated for these tasks. Specifically,
we evaluate Detectron2's implementation of Faster R-CNN using different base
models and configurations. We also experiment with these approaches using the
Global Road Damage Detection Challenge 2020, A Track in the IEEE Big Data 2020
Big Data Cup Challenge dataset. The results show that the X101-FPN base model
for Faster R-CNN with Detectron2's default configurations are efficient and
general enough to be transferable to different countries in this challenge.
This approach results in F1 scores of 51.0% and 51.4% for the test1 and test2
sets of the challenge, respectively. Though the visualizations show good
prediction results, the F1 scores are low. Therefore, we also evaluate the
prediction results against the existing annotations and discover some
discrepancies. Thus, we also suggest strategies to improve the labeling process
for this dataset.
