  Classical video coding for satisfying humans as the final user is a widely
investigated field of studies for visual content, and common video codecs are
all optimized for the human visual system (HVS). But are the assumptions and
optimizations also valid when the compressed video stream is analyzed by a
machine? To answer this question, we compared the performance of two
state-of-the-art neural detection networks when being fed with deteriorated
input images coded with HEVC and VVC in an autonomous driving scenario using
intra coding. Additionally, the impact of the three VVC in-loop filters when
coding images for a neural network is examined. The results are compared using
the mean average precision metric to evaluate the object detection performance
for the compressed inputs. Throughout these tests, we found that the
Bj{\o}ntegaard Delta Rate savings with respect to PSNR of 22.2 % using VVC
instead of HEVC cannot be reached when coding for object detection networks
with only 13.6% in the best case. Besides, it is shown that disabling the VVC
in-loop filters SAO and ALF results in bitrate savings of 6.4 % compared to the
standard VTM at the same mean average precision.
