Fast Person Detection Using YOLOX With AI Accelerator For Train Station Safety
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Abstract (arXiv)
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).
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 16 references that are in the map. First posted on arXiv 11 Feb 2026.
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9 of 16 references.
References · 9
- 2014Microsoft COCO: Common Objects in Contextcited by 17,498
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2017Mask R-CNNcited by 7,259
- 2015SSD: Single Shot MultiBox Detectorcited by 3,125
- 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
- 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,064
- 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
- 2019A Survey of Deep Learning-based Object Detectioncited by 106
- 2019UG2+ Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environmentscited by 84
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