轨道交通视频中乘客口罩佩戴检测算法研究

Detection Algorithm of Passengers Wearing Masks in Rail Transit Video

为应对新冠疫情下乘客乘坐轨道交通时必须佩戴口罩的情况,提出一种基于深度学习的轻量化口罩检测算法(Mask-Det算法)。首先,使用轻量化骨干网络EfficientNet提取图像特征;然后,利用高效的特征融合模块增强用于检测小目标的浅层特征图的语义信息;最后,算法在公共场景数据集上训练,并使用迁移学习在轨道交通数据集上做进一步优化。Mask-Det算法检测准确率高、模型参数小、检测速度快,可以实时检测各场所乘客是否佩戴口罩,有效减轻工作人员压力,提高进站速度。

In response to the mandatory situation of passengers wearing masks on rail transit during COVID-19 pandemic period,a lightweight mask detection algorithm(Mask-Det)based on deep learning is proposed.First,the lightweight backbone network EfficientNet is used to extract image features.Then,a highly efficient feature fusion module is used to enhance the semantic information of the shallow feature map for detecting small targets.Finally,the algorithm is trained on the dataset of public scenarios,and then further optimized on the dataset of rail transit scenarios using transfer learning.Mask-Det algorithm has high detection accuracy,small model parameters,and fast detection speed.The algorithm can detect in real time whether passengers are wearing masks at various places,thus effectively alleviate personnel stress and improve passenger entry speed.