用YOLOX与Hailo-8加速器提升火车站行人检测速度与准确率。
Fast Person Detection Using YOLOX With AI Accelerator For Train Station Safety
- 基于YOLOX模型结合Hailo-8边缘加速器实现快速行人检测。
- 相比Jetson Orin Nano，精度提升超12%，延迟降低20毫秒。
- 适合需要低延迟高精度的智能交通安防场景。
近年来，图像处理技术快速发展并广泛应用于医疗、工业及交通等领域。在交通领域，目标检测被用于提升安全性，如交通监管和火车站乘客过轨安全。车站站台区域常发生乘客越过黄线等安全隐患，亟需进一步技术手段降低事故率。本文聚焦于使用YOLOX模型与边缘AI加速器实现火车站乘客检测，对比了Hailo-8与Jetson Orin Nano硬件性能。实验结果表明，Hailo-8在准确率上优于Jetson Orin Nano（提升超过12%），同时延迟降低20毫秒，具备更强实时性与可靠性。
原文摘要 · Abstract (English)
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).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。