Traffic Accident Detection based on Multimodal Knowledge Graphs

To rapidly identify traffic accidents, reduce the manpower required in traffic management, and enhance commuting efficiency, this paper proposes a traffic accident detection method based on a multimodal knowledge graph. By integrating data information from various forms such as images, videos, etc., a multimodal knowledge graph is constructed, and relational graph convolutional networks (R-GCN) are applied for knowledge graph reasoning. Results from testing on multiple datasets validate the superiority of this method in traffic accident detection, particularly excelling in analysis accuracy compared to traditional neural network approaches.