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.

我们展示了在复杂交通场景中将语义一致性的概念和知识感知的重新优化方法应用于目标检测问题。此外，我们介绍了一种从具有实例级注释的图像数据集中提取知识图谱的新方法，并将该知识图谱与现有的语义一致性模型集成。将这种新型混合知识图谱和频率分析和外部知识图谱作为语义信息的源与 Faster-RCNN 和 DETR 目标检测模型的现有方法结合，我们研究了知识感知的重新优化对所研究的模型和方法的效果。我们发现，在所有模型和方法的组合中，使用这种方法可以实现有限但一致的准确性和 / 或召回率的提升。