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Knowledge-aware Object Detection in Traffic Scenes

Jean-Francois Nies; Syed Tahseen Raza Rizvi; Mohsin Munir; Ludger van Elst; Andreas Dengel
In: INSTICC (Hrsg.). Proceedings of the 16th International Conference on Agents and Artificial Intelligence. International Conference on Agents and Artificial Intelligence (ICAART-2024), February 24-26, Rome (ONLINE), Italy, ISBN 978-989-758-680-4, SciTePress, 2/2024.


Autonomous driving is a widely popular domain that empowers the autonomous vehicle to make crucial decisions in a constantly evolving traffic scenario. The role of perception is pivotal in the secure operation of the autonomous vehicle in a complex traffic scene. Recently, several approaches have been proposed for the task of object detection. 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. Moreover, we also introduce a novel method for extracting a knowledge graph encoding the semantic relationship between the traffic participants from an autonomous driving dataset. We also conducted an investigation into the efficacy of utilizing diverse knowledge graph generation methodologies and in- and out-domain knowledge sources on the efficacy of the outcomes. Finally, we investigated the effectiveness of knowledge-aware re-optimizat ion on the Faster-RCNN and DETR object detection models. Results suggest that modest but consistent improvements in precision and recall can be achieved using this method.


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