  Mobile machines typically working in a closed site, have a high potential to
utilize autonomous driving technology. However, vigorously thriving development
and innovation are happening mostly in the area of passenger cars. In contrast,
although there are also many research pieces about autonomous driving or
working in mobile machines, a consensus about the SOTA solution is still not
achieved. We believe that the most urgent problem that should be solved is the
absence of a public and challenging visual dataset, which makes the results
from different researches comparable. To address the problem, we publish the
KIT MOMA dataset, including eight classes of commonly used mobile machines,
which can be used as a benchmark to evaluate the SOTA algorithms to detect
mobile construction machines. The view of the gathered images is outside of the
mobile machines since we believe fixed cameras on the ground are more suitable
if all the interesting machines are working in a closed site. Most of the
images in KIT MOMA are in a real scene, whereas some of the images are from the
official website of top construction machine companies. Also, we have evaluated
the performance of YOLO v3 on our dataset, indicating that the SOTA computer
vision algorithms already show an excellent performance for detecting the
mobile machines in a specific working site. Together with the dataset, we also
upload the trained weights, which can be directly used by engineers from the
construction machine industry. The dataset, trained weights, and updates can be
found on our Github. Moreover, the demo can be found on our Youtube.
