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

TL;DR

This paper presents a fast and accurate passenger detection system at train stations using YOLOX with AI accelerators, comparing Hailo-8 and Jetson Orin Nano for improved security and safety.

Contribution

It demonstrates the effectiveness of Hailo-8 AI hardware in enhancing YOLOX-based passenger detection accuracy and reducing latency at train stations.

Findings

Hailo-8 achieves over 12% higher accuracy than Jetson Orin Nano.

Hailo-8 reduces detection latency by 20 ms compared to Jetson Orin Nano.

The system improves train station safety through faster, more accurate passenger detection.

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).