Accurate target recognition and location is one of the key technologies in the field of smart city application. In order to solve the problem of large pedestriain flow impact in crowded metro stations, a method of in-depth learning detection based on SSD (single shot multibox detector) is proposed. The algorithm extracts the feature information of the input image, then returns the boundary box of the location on the feature map and classifies the object categories. Using the method of local feature extraction, the features of different positions, different aspect ratios and sizes are obtained, and VGG16 is used as the base network to optimise and improve the network structure. The results of simulation experiments on VOC2007 and data_sub show that the maximum value of mAP is 77% and the highest accuracy is 96.31%. Compared with other mainstream deep learning target detection methods, SSD has higher accuracy, better real-time and robustness. It can solve the problem of different pedestrian target sizes and better realise pedestrians in subway station environment. Detection provides decision-making basis for flow statistics.

中文翻译：

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基于SSD模型的地铁行人检测算法研究

准确的目标识别和定位是智慧城市应用领域的关键技术之一。为解决地铁拥挤情况下行人流影响大的问题，提出了一种基于SSD（单发多盒检测器）的深度学习检测方法。该算法提取输入图像的特征信息，然后返回特征图上该位置的边界框并分类对象类别。利用局部特征提取的方法，获得了不同位置，不同长宽比和大小的特征，并以VGG16作为基础网络对网络结构进行了优化和改进。在VOC2007和data_sub上进行的仿真实验结果表明，mAP的最大值为77％，最高精度为96.31％。与其他主流深度学习目标检测方法相比，SSD具有更高的准确性，更好的实时性和鲁棒性。它可以解决行人目标尺寸不同的问题，更好地实现地铁站环境下的行人。检测为流量统计提供决策依据。