This paper presents a multimodal sensing-based system designed to improve the accuracy and real-time performance of detecting electric vehicles (EVs) attempting to enter elevators. The system architecture integrates a data acquisition layer, an analysis and processing layer, an intelligent decision layer, and an application layer. By employing sensors such as flexible tactile arrays and visual cameras, the system captures multimodal data. Detection results are generated through a global scene context information extraction module and a local spatio-temporal feature extraction module. Experimental outcomes indicate that the proposed system achieves a mean average precision (mAP) of 95.60%, operates at 31.5 frames per second (FPS), and maintains a model size of 26 MB. Under occluded conditions, it attains average mAP@.5 and mAP@.5:.95 scores of 91.2% and 80.9%, respectively, surpassing other existing detection methods. The results demonstrate that this multimodal sensing system can effectively mitigate safety risks associated with EVs entering elevators, offering high practical utility and reliability.