Detection Algorithm for Multimodal Data Fusion in Smart Transportation

Intelligent transportation systems are an important solution to address transportation issues. Traditional traffic management methods are no longer able to meet the demands of modern cities for efficient, safe, and intelligent transportation systems. In this paper, we introduce intelligent transportation systems as a solution and utilize information technology and communication technology to improve traffic management efficiency, enhance traffic safety, and optimize traffic flow. Furthermore, we address the issues of large bandwidth requirements, low efficiency, and accuracy in data transmission in smart transportation applications. We introduce multimodal data understanding techniques to convert complex raw multimodal data into semantic information for compression and transmission, thereby reducing bandwidth requirements and improving the efficiency and accuracy of data transmission. Using multimodal data understanding techniques, key scene information can be extracted from video, image, and sensor data for comprehensive analysis and intelligent processing. Based on these findings, we propose a multimodal data fusion and understanding algorithm for intelligent transportation systems, which optimizes the entire data processing process through accurate data fusion and understanding, as well as multi-task processing optimization. Experimental results demonstrate that this method is effective in handling traffic problems and achieves excellent results in different traffic environments, with higher accuracy compared to traditional algorithms. The ablation experiment results further validate the effectiveness of this method and the rationality of parameter settings.