Passenger Flow Detection in Subway Stations Based on Improved You Only Look Once Algorithm

Document Type

Journal Article

Publication Date


Subject Area

place - asia, mode - subway/metro, ridership - behaviour, ridership - modelling, planning - personal safety/crime, technology - intelligent transport systems


data and data science, artificial intelligence and advanced computing applications, machine vision, public transportation, rail transit systems, subway, rail, passenger rail transportation, passengers


Passenger flow detection plays an important role in guaranteeing passenger safety. It contributes to the efficiency of passenger flow control at stations. However, passenger flow detection in subway stations has the problems of target size inconsistency and poor detection effects of small targets. To solve this problem, an improved You Only Look Once algorithm (Improved-YOLO) is proposed based on the YOLOv4 for passenger flow detection in subway stations. The repeatable bidirectional feature fusion (BiFF) module was designed to combine with the adaptively spatial feature fusion (ASFF) module to replace the feature fusion network of the YOLOv4. To verify the effectiveness of the Improved-YOLO, the passenger flow dataset of Nanning Metro Line 1 was used for the experiment. Augmentation and transfer learning were then used to improve the performance of the model. Compared with the YOLOv4, the results showed that the mean average accuracy (mAP) of the Improved-YOLO increased from 89.63% to 92.96%, and the detection time of a single frame image reduced from 61 ms to 52 ms. Compared with other classical models, the Improved-YOLO showed satisfactory performance in passenger flow detection in subway stations. These experimental results can provide reference and theoretical support for real-time passenger flow detection and passenger behavior recognition in subways.


Permission to publish the abstract has been given by SAGE, copyright remains with them.