摘要： 针对行人交通安全问题，开发行人检测系统以提醒行人和司机危险的发生。对目标检测神经网络模型进行分析和对比实验，选取以darknet为网络框架的YOLO-fastest模型进行改进优化并采用分类并标签的实时交通数据进行训练，最终将训练模型部署至开发板完成实时性检测并能够根据车辆速度反馈给行人危险信号。实验结果表明YOLO-fastest模型的平均检测精度为96.1%，检测速度为33 f/s，模型大小为1.2 MB，既满足检测精度又满足检测速度的要求，能够完成对真实交通场景下的实时性检测。

中图分类号： TP391.4；TP183
文献标识码： A
DOI： 10.19358/j.issn.2096-5133.2021.12.010
引用格式： 刘佳丽，黄世震，何恩德. 基于ARM和深度学习的智能行人预警系统[J].信息技术与网络安全，2021，40(12)：60-64.

Intelligent pedestrian warning system based on ARM and deep learning

Abstract： To address the problem of pedestrian traffic safety, a pedestrian detection system is developed to alert pedestrians and drivers to the danger of occurrence. The target detection neural network model is analyzed and compared with the experiments, and the YOLO-fastest model with darknet as the network framework is selected for improvement and optimization and trained with classified and labeled real-time traffic data. The training model is finally deployed to the development board to complete real-time detection and to provide pedestrian danger signals based on vehicle speed. The experimental results show that the average detection accuracy of YOLO-fastest model is 96.1%, the detection speed is 33 f/s, and the model size is 1.2 MB to meet the requirements of both detection accuracy and detection speed to complete the real-time detection of real traffic scenarios.

Key words : pedestrian safety；target detection；neural network；YOLO-fastest algorithm