在物联网边缘设备上实现低延迟行人检测，推理仅需147毫秒。
Real-Time Pedestrian Detection on IoT Edge Devices: A Lightweight Deep Learning Approach
- 优化YOLO模型轻量化部署，适配边缘计算资源
- 实测推理速度147毫秒，帧率2.3帧/秒，准确率达78%
- 适合智能交通、自动驾驶等实时安全场景
人工智能已深度融入日常生活。计算机视觉技术发展到可承担智能交通系统中路口行人检测这一关键安全任务，并向车辆发出潜在碰撞预警。传统中心化计算通过分析摄像头数据生成警报并发送给周边车辆，但实时应用面临延迟高、数据传输慢及生命损失风险等问题。边缘服务器提供本地化计算与存储，降低响应时间，但其算力受限。轻量级深度学习（DL）技术使边缘服务器能运行压缩的深度神经网络（DNN）模型。本研究探索在人工智能物联网（AIoT）边缘设备上部署轻量级DL模型。采用优化的基于YOLO的深度学习模型实现实时行人检测，检测事件通过消息队列遥测传输（MQTT）协议传至边缘服务器。仿真结果表明，优化后的YOLO模型可实现实时行人检测，推理速度达147毫秒，帧率为2.3帧每秒，准确率为78%，相比基线模型有显著提升。
原文摘要 · Abstract (English)
Artificial intelligence (AI) has become integral to our everyday lives. Computer vision has advanced to the point where it can play the safety critical role of detecting pedestrians at road intersections in intelligent transportation systems and alert vehicular traffic as to potential collisions. Centralized computing analyzes camera feeds and generates alerts for nearby vehicles. However, real-time applications face challenges such as latency, limited data transfer speeds, and the risk of life loss. Edge servers offer a potential solution for real-time applications, providing localized computing and storage resources and lower response times. Unfortunately, edge servers have limited processing power. Lightweight deep learning (DL) techniques enable edge servers to utilize compressed deep neural network (DNN) models. The research explores implementing a lightweight DL model on Artificial Intelligence of Things (AIoT) edge devices. An optimized You Only Look Once (YOLO) based DL model is deployed for real-time pedestrian detection, with detection events transmitted to the edge server using the Message Queuing Telemetry Transport (MQTT) protocol. The simulation results demonstrate that the optimized YOLO model can achieve real-time pedestrian detection, with a fast inference speed of 147 milliseconds, a frame rate of 2.3 frames per second, and an accuracy of 78%, representing significant improvements over baseline models.
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