为了实时检测乘客的异常行为，提出一种基于YOLOv5s算法的轻量化自动扶梯乘客异常行为实时检测算法YOLO-STE。首先在主干网络中引入轻量化ShuffleNetV2网络，以减少主干网络的参数量和计算量；其次在骨干网络的最后一层引入基于Transformer编码的C3TR模块，以更好地提取丰富的全局信息和融合不同尺度的特征；最后在YOLOv5s的特征融合网络中嵌入SE（Squeeze-and-excitation）注意力机制，以更好地关注主要信息，从而提高模型精度。自建数据集并进行实验，实验结果表明，相比于原YOLOv5s，改进算法的全类平均精度值（mAP）高出1.9百分点，达到了96.1%，模型大小减少了70.8%。并且在Jetson Nano硬件上部署测试所得，改进后的算法前传耗时比原YOLOv5s模型缩短了39.9%。通过对比改进前后的算法，后者能更好地实现对自动扶梯乘客异常行为的实时检测，从而可以更好地保障乘客乘梯安全。

To detect passengers' abnormal behavior in real time, we propose a lightweight escalator passenger' abnormal behavior real-time detection algorithm, YOLO-STE, based on YOLOv5s. First, a lightweight ShuffleNetV2 network was introduced in the backbone network to reduce the number of parameters and its computation. Second, a C3TR module based on Transformer encoding was introduced in the last layer of the backbone network to better extract rich global information and fuse features at different scales. Finally, an SE (Squeeze-and-excitation) attention mechanism was embedded in the feature fusion network of YOLOv5s to better focus on the main information and improve the model accuracy. We developed our dataset and conducted experiments. The experimental results demonstrate that compared with the original YOLOv5s, the mean Average Precision (mAP) of the improved algorithm is 1.9 percentage points higher, reaching 96.1%, and the model size is reduced by 70.8%. Moreover, the improved algorithm's forward propagation time is 39.9% shorter than that of the original YOLOv5s model when deployed and tested on the Jetson Nano hardware. Compared with the original YOLOv5s model, the improved algorithm can better achieve real-time detection of abnormal behavior of escalator passengers, which can better ensure the safety of passengers riding the escalator.