A Deep Multiobject Detection Model for Passenger Escalator Safety
提出一个基于剪枝YOLOv7-Tiny的轻量级实时目标检测系统，部署在NVIDIA Jetson Nano上，识别扶梯上的危险物品（如高跟鞋、长裙、行李箱等）并发出警报，mAP达94.69%，适合资源受限的公共交通环境。
Accidents involving escalators in mass rapid transit (MRT) systems pose a serious risk to public safety, often resulting from clothing or footwear getting caught, or large items toppling during movement. Despite the availability of passive warnings, such as signage and audio announcements, these methods often go unnoticed by commuters and lack the ability to adapt to real-time risks. Existing computer vision solutions are either too computationally intensive for deployment on edge devices or lack sufficient accuracy for practical use. To address these challenges, this study proposes a real-time, lightweight object detection system using a pruned YOLOv7-Tiny model, optimized for deployment on the NVIDIA Jetson Nano edge computing platform. The system is designed to identify safety-critical items, such as general footwear, high heels, long skirts, suitcases, strollers, and shopping trolleys, in real-time. Upon detection, it issues visual and auditory alerts, and in cases involving large items, sends email notifications to station personnel. Model pruning significantly reduces computational overhead while maintaining high accuracy. Experimental results demonstrate that the system achieves a mean average precision (mAP) of 94.69%, outperforming conventional detection models while maintaining real-time performance. These results highlight the system’s potential for enhancing passenger safety and operational efficiency in resource-constrained public transit environments.