Real-Time Pedestrian Detection on IoT Edge Devices: A Lightweight Deep Learning Approach

TL;DR

This paper presents a lightweight YOLO-based deep learning model optimized for real-time pedestrian detection on IoT edge devices, addressing latency and processing constraints in intelligent transportation systems.

Contribution

It introduces an optimized YOLO model tailored for AIoT edge devices, enabling efficient real-time pedestrian detection with improved speed and accuracy.

Findings

Inference speed of 147 ms per frame

Detection frame rate of 2.3 fps

Detection accuracy of 78%

Abstract

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.