Fast Person Detection Using YOLOX With AI Accelerator For Train Station Safety

2024-08-06

Asia Pacific Symposium on Intelligent and Evolutionary Systems

AI Summary

This work proposes a real-time pedestrian monitoring system based on the YOLOX object detection model to enhance platform safety in railway stations and prevent hazardous behaviors such as passengers crossing the yellow safety line. The study presents the first deployment of YOLOX on the Hailo-8 edge AI accelerator and provides a systematic comparison with the Jetson Orin Nano platform. Experimental results demonstrate that, in this specific application scenario, the Hailo-8 not only achieves a precision improvement of over 12% but also reduces inference latency by 20 milliseconds compared to existing edge platforms. These findings underscore the superior efficiency and practicality of the Hailo-8 for real-time safety surveillance in rail transit environments.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Object Detection & Categorization
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

Recently, Image processing has advanced Faster and applied in many fields, including health, industry, and transportation. In the transportation sector, object detection is widely used to improve security, for example, in traffic security and passenger crossings at train stations. Some accidents occur in the train crossing area at the station, like passengers uncarefully when passing through the yellow line. So further security needs to be developed. Additional technology is required to reduce the number of accidents. This paper focuses on passenger detection applications at train stations using YOLOX and Edge AI Accelerator hardware. the performance of the AI accelerator will be compared with Jetson Orin Nano. The experimental results show that the Hailo-8 AI hardware accelerator has higher accuracy than Jetson Orin Nano (improvement of over 12%) and has lower latency than Jetson Orin Nano (reduced 20 ms).

Problem

• person detection
• train station safety
• object detection
• accident prevention
• yellow line violation

Innovation

• YOLOX
• AI accelerator
• person detection
• edge AI
• train station safety