Enhancing transportation network intelligence through visual scene feature clustering analysis with 3D sensors and adaptive fuzzy control

The complex environments and unpredictable states within transportation networks have a significant impact on their operations. To enhance the level of intelligence in transportation networks, we propose a visual scene feature clustering analysis method based on 3D sensors and adaptive fuzzy control to address the various complex environments encountered. Firstly, we construct a feature extraction framework for visual scenes using 3D sensors and employ a series of feature processing operators to repair cracks and noise in the images. Subsequently, we introduce a feature aggregation approach based on an adaptive fuzzy control algorithm to carefully screen the preprocessed features. Finally, by designing a similarity matrix for the transportation network environment, we obtain the recognition results for the current environment and state. Experimental results demonstrate that our method outperforms competitive approaches with a mean average precision (mAP) value of 0.776, serving as a theoretical foundation for visual scene perception in transportation networks and enhancing their level of intelligence.