Chaehyeon Kim, Sara Yu, Ki Yong Lee, Journal of Information Processing Systems Vol. 20, No. 6, pp. 827-840, Dec. 2024  

https://doi.org/10.3745/JIPS.04.0329
Keywords: Electric Scooter Sharing Service, Object Detection, Photo Recognition, YOLO
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Abstract

The use of electric scooter (e-scooter) sharing services has increased significantly in recent years due to their convenience and economy. In order to rent an e-scooter, a user first finds nearby e-scooters using a smartphone application, which shows the global positioning system (GPS) locations of e-scooters around the user. However, since the error of GPS can be more than 10 m, the user may have difficulty finding the exact location of the escooter the user wants to use. To alleviate this problem, an e-scooter sharing service “Kickgoing,” operated by Olulo in South Korea, provides users with e-scooter photos taken by users upon return, along with their GPS locations, on its smartphone application. Those photos help subsequent users to find e-scooters more accurately. However, since some users upload photos that do not include e-scooters or are unrecognizable, it is essential to provide users with only those photos that clearly include an e-scooter. Therefore, in this paper, we develop an e-scooter photo recognition system that can accurately recognize only those photos that include e-scooters. The developed system, which is based on YOLO, uses three techniques: if a whole e-scooter is not recognized, it recognizes an e-scooter by recognizing its parts individually; it recognizes e-scooters with significantly different photography angles as different classes; and it provides users with only those photos in which the proportion of the e-scooter is within a certain range. Experimental results on a real dataset show that the developed system recognizes e-scooter photos more accurately compared to a system that uses the YOLO model as is.

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