Fast Person Detection Using YOLOX With AI Accelerator For Train Station Safety
This work addresses safety issues at train stations for passengers, but it is incremental as it applies an existing method to a specific domain with hardware comparisons.
This paper tackled the problem of passenger detection at train stations to improve safety by using YOLOX with an AI accelerator, achieving over 12% higher accuracy and 20 ms lower latency compared to Jetson Orin Nano.
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).