Bicycle riding environment identification for detecting traffic violation in a riding safety support information system

This paper proposes a method for identifying the bicycle riding environment using only onboard equipment. Initially, a fundamental subsystem is established for identifying the bicycle riding environment, and its functionality is validated. The findings indicate that the subsystem, utilizing an open-source trained model, can detect riding on roadways but not on sidewalks. Consequently, we emphasize the need for transfer learning, specifically using sidewalk viewpoint images, to enable the identification of bicycle riding environments. Subsequently, we conduct bicycle riding environment identification by employing a transfer learning model with manually labeled training data. The results demonstrate that after transfer learning, sidewalk riding detection, which was previously unachievable, becomes feasible. The identification rate was over 80%. Furthermore, we develop four riding environment identification algorithms, including the transfer learning model, and compare their performance across various road environments and riding conditions. Consequently, it is established that the region of interest (ROI) extension identification algorithm exhibits the highest identification performance (93% on average). As a result, this paper contributes valuable insights into the realization of bicycle riding environment identification, particularly in the context of detecting traffic violations within the riding safety support information system.