  In this paper, we demonstrate that the concept of Semantic Consistency and
the ensuing method of Knowledge-Aware Re-Optimization can be adapted for the
problem of object detection in intricate traffic scenes. Furthermore, we
introduce a novel method for extracting a knowledge graph from a dataset of
images provided with instance-level annotations, and integrate this new
knowledge graph with the existing semantic consistency model. Combining both
this novel hybrid knowledge graph and the preexisting methods of frequency
analysis and external knowledge graph as sources for semantic information, we
investigate the effectiveness of knowledge-aware re-optimization on the
Faster-RCNN and DETR object detection models. We find that limited but
consistent improvements in precision and or recall can be achieved using this
method for all combinations of model and method studied.
